Experiment with Random Difficulty (results based on the bet)
Removing Outliers
## [1] "Outliers : 3qq8dp8jk, 79pn8m6v8, e58u3sinl, hudayxdge, w2x28nknu"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 5"## [1] "Total number of outliers motor task: 1"## [1] "Total number of outliers perceptive task: 1"## [1] "Total number of outliers logical task: 3"Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2268.1 2290.2 -1130.0 2260.1 1877
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9846 -0.7313 0.2308 0.7546 2.8895
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5178 0.7196
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1555 0.1548 -7.464 8.42e-14 ***
## difficulty 3.0512 0.2019 15.113 < 2e-16 ***
## timeNorm -0.3871 0.1728 -2.241 0.0251 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.488
## timeNorm -0.430 -0.167## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1881 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-0.9973563
## 1st Qu.:-0.4243437
## Median :-0.1362009
## Mean :-0.0003041
## 3rd Qu.: 0.3781255
## Max. : 1.6570924
## [1] "Intercept: -1.16 8.4e-14 ***"
## [1] "Difficulty: 3.05 1.3e-51 ***"
## [1] "Time: -0.387 0.025 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2300"## 0% 25% 50% 75% 100%
## -1.6570924 -0.3781255 0.1362009 0.4243437 0.9973563
## 0% 25% 50% 75% 100%
## -1.6570924 -0.3781255 0.1362009 0.4243437 0.9973563
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1535.4 1557.4 -763.7 1527.4 1811
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2914 -0.4479 0.1164 0.3982 4.7670
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5772 0.7598
## Number of obs: 1815, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2311 0.1826 -12.217 < 2e-16 ***
## difficulty 7.0302 0.3250 21.631 < 2e-16 ***
## timeNorm -1.0832 0.2369 -4.572 4.84e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.458
## timeNorm -0.385 -0.358## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1815
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.527169
## 1st Qu.:-0.388916
## Median :-0.005975
## Mean : 0.002363
## 3rd Qu.: 0.374680
## Max. : 1.350107
## [1] "Intercept: -2.23 2.5e-34 ***"
## [1] "Difficulty: 7.03 9.1e-104 ***"
## [1] "Time: -1.08 4.8e-06 ***"
## [1] "R2 fixed: 0.55"
## [1] "R2 mixed: 0.62"
## [1] "Cross Val: 0.81"
## [1] "AIC: 1500"## 0% 25% 50% 75% 100%
## -1.350106912 -0.374679910 0.005974618 0.388915836 1.527169116
## 0% 25% 50% 75% 100%
## -1.350106912 -0.374679910 0.005974618 0.388915836 1.527169116
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1816.7 1838.8 -904.3 1808.7 1877
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7618 -0.5239 -0.1972 0.5160 5.0573
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.067 1.033
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6536 0.1924 -8.596 <2e-16 ***
## difficulty 5.4305 0.2647 20.515 <2e-16 ***
## timeNorm -2.0774 0.2224 -9.340 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.388
## timeNorm -0.276 -0.437## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1881 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.492039
## 1st Qu.:-0.741161
## Median :-0.213560
## Mean : 0.004668
## 3rd Qu.: 0.599760
## Max. : 2.373359
## [1] "Intercept: -1.65 8.2e-18 ***"
## [1] "Difficulty: 5.43 1.6e-93 ***"
## [1] "Time: -2.08 9.6e-21 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.53"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1800"## 0% 25% 50% 75% 100%
## -2.3733594 -0.5997602 0.2135598 0.7411607 1.4920388
## 0% 25% 50% 75% 100%
## -2.3733594 -0.5997602 0.2135598 0.7411607 1.4920388
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0832, p-value = 0.2787
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1121498
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.27984, p-value = 0.7796
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02959975
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.18429, p-value = 0.8538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01913758Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.92279, p-value = 0.3561
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09432639
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.40055, p-value = 0.6887
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04164333
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.83074, p-value = 0.4061
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.08524489Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02429648## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.5588, p-value = 0.0105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3482495
##
## [1] "self.eff.on.level.s 0.35 0.011 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.77294, p-value = 0.4396
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1034345Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2531, p-value = 0.2102
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1232133
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9255, p-value = 0.05417
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1918732
##
## [1] "risk.av.on.level.s 0.19 0.054 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0617, p-value = 0.2884
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1042971Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.0129, p-value = 0.3111
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09643322## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.0949, p-value = 0.03618
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2036664
##
## [1] "age.on.level.s 0.2 0.036 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2495, p-value = 0.2115
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1192254Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.3361, p-value = 0.01949
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.257113
##
## [1] "sexe.on.level.m -0.26 0.019 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0, p-value = 1
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.18884, p-value = 0.8502
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02078441
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 223, p-value = 0.01897
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.85282846 -0.09534056
## sample estimates:
## difference in location
## -0.5051082
##
## [1] "sexe.on.level.m.2 -0.51 0.019 * mean(A): 0.16 mean(B): -0.32"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 333, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.3670949 0.4731302
## sample estimates:
## difference in location
## -0.0009246191
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 340, p-value = 0.8583
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7335260 0.5047401
## sample estimates:
## difference in location
## -0.02802612Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.62185, p-value = 0.534
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03720939Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -3.4464, p-value = 0.0005681
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2033235
##
## [1] "pbg.on.error -0.2 0.00057 ***"In game level and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.44873, p-value = 0.6536
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02338143
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.23405, p-value = 0.8149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02130326
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.094374, p-value = 0.9248
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.008754209
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.45433, p-value = 0.6496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04135338Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 4.1645, p-value = 3.12e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2646112
##
## [1] "sexe.on.error 0.26 3.1e-05 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.3699, p-value = 0.01779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2608393
##
## [1] "sexe.on.error.m 0.26 0.018 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.565, p-value = 0.01032
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2875846
##
## [1] "sexe.on.error.s 0.29 0.01 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.2318, p-value = 0.02563
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2456339
##
## [1] "sexe.on.error.l 0.25 0.026 *"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 4376, p-value = 3.143e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.04977679 0.13237866
## sample estimates:
## difference in location
## 0.09299933
##
## [1] "sexe.on.error.2 0.093 3.1e-05 *** mean(A): -0.093 mean(B): 0.001"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 501, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01355287 0.15331497
## sample estimates:
## difference in location
## 0.09290042
##
## [1] "sexe.on.error.m.2 0.093 0.017 * mean(A): -0.085 mean(B): 0.0073"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 476, p-value = 0.009655
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.02092227 0.15744127
## sample estimates:
## difference in location
## 0.09796631
##
## [1] "sexe.on.error.s.2 0.098 0.0097 ** mean(A): -0.1 mean(B): -0.0014"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 481, p-value = 0.02523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.009389481 0.150561466
## sample estimates:
## difference in location
## 0.09060751
##
## [1] "sexe.on.error.l.2 0.091 0.025 * mean(A): -0.091 mean(B): -0.0033"Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.60676, p-value = 0.544
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03431688
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.12035, p-value = 0.9042
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01183404
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.11152, p-value = 0.9112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01111235
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.79275, p-value = 0.4279
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07787518Self efficacy and subjective difficulty error
## Warning: Removed 84 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.9644, p-value = 0.003033
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2277125
##
## [1] "self.eff.on.error -0.23 0.003 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.7653, p-value = 0.07751
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2402652
##
## [1] "self.eff.on.error -0.24 0.078 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2240675
##
## [1] "self.eff.on.error -0.22 0.1 :("## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6401, p-value = 0.101
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2194829Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0096 47 0.64 :(
## 2: 0.09375 0.0440 54 0.052 .
## 3: 0.15625 0.0045 58 0.91 :(
## 4: 0.21875 0.0260 58 0.27 :(
## 5: 0.28125 0.0044 57 0.98 :(
## 6: 0.34375 -0.0400 58 0.25 :(
## 7: 0.40625 -0.0400 58 0.23 :(
## 8: 0.46875 -0.0045 58 0.94 :(
## 9: 0.53125 -0.0190 58 0.54 :(
## 10: 0.59375 -0.0420 58 0.18 :(
## 11: 0.65625 -0.0370 58 0.31 :(
## 12: 0.71875 -0.1100 58 1.9e-05 ***
## 13: 0.78125 -0.1400 58 7.4e-08 ***
## 14: 0.84375 -0.2100 58 1.7e-09 ***
## 15: 0.90625 -0.1900 57 5e-11 ***
## 16: 0.96875 -0.1800 55 1.1e-10 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 47 0.64 :(
## 2: 54 0.052 .
## 3: 58 0.91 :(
## 4: 58 0.27 :(
## 5: 57 0.98 :(
## 6: 58 0.25 :(
## 7: 58 0.23 :(
## 8: 58 0.94 :(
## 9: 58 0.54 :(
## 10: 58 0.18 :(
## 11: 58 0.31 :(
## 12: 58 1.9e-05 ***
## 13: 58 7.4e-08 ***
## 14: 58 1.7e-09 ***
## 15: 57 5e-11 ***
## 16: 55 1.1e-10 ***
## [1] 56.8
## [1] 2.86
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0240 32 0.15 :(
## 2: 0.09375 0.0390 34 0.19 :(
## 3: 0.15625 -0.0310 42 0.35 :(
## 4: 0.21875 -0.0045 40 0.79 :(
## 5: 0.28125 -0.0190 38 0.6 :(
## 6: 0.34375 -0.0290 37 0.72 :(
## 7: 0.40625 0.0100 36 0.87 :(
## 8: 0.46875 0.0670 38 0.1 :(
## 9: 0.53125 0.0640 40 0.22 :(
## 10: 0.59375 -0.0220 39 0.81 :(
## 11: 0.65625 -0.0130 35 0.76 :(
## 12: 0.71875 -0.1400 37 0.0024 **
## 13: 0.78125 -0.1300 37 0.0039 **
## 14: 0.84375 -0.2200 29 4.2e-05 ***
## 15: 0.90625 -0.1900 22 4.1e-05 ***
## 16: 0.96875 -0.1500 11 0.0035 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 32 0.15 :(
## 2: 34 0.19 :(
## 3: 42 0.35 :(
## 4: 40 0.79 :(
## 5: 38 0.6 :(
## 6: 37 0.72 :(
## 7: 36 0.87 :(
## 8: 38 0.1 :(
## 9: 40 0.22 :(
## 10: 39 0.81 :(
## 11: 35 0.76 :(
## 12: 37 0.0024 **
## 13: 37 0.0039 **
## 14: 29 4.2e-05 ***
## 15: 22 4.1e-05 ***
## 16: 11 0.0035 **
## [1] 34.2
## [1] 7.86
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 24 0.015 *
## 2: 0.09375 0.0190 33 0.47 :(
## 3: 0.15625 -0.0660 39 0.13 :(
## 4: 0.21875 -0.0045 43 0.52 :(
## 5: 0.28125 -0.0310 43 0.66 :(
## 6: 0.34375 -0.0940 37 0.041 *
## 7: 0.40625 -0.1200 44 0.069 .
## 8: 0.46875 -0.0710 42 0.11 :(
## 9: 0.53125 -0.0810 41 0.19 :(
## 10: 0.59375 -0.0940 38 0.089 .
## 11: 0.65625 -0.0130 42 0.68 :(
## 12: 0.71875 -0.0960 41 0.028 *
## 13: 0.78125 -0.1400 43 0.00017 ***
## 14: 0.84375 -0.1900 42 6e-06 ***
## 15: 0.90625 -0.2000 39 5.3e-08 ***
## 16: 0.96875 -0.2000 37 1.1e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 24 0.015 *
## 2: 33 0.47 :(
## 3: 39 0.13 :(
## 4: 43 0.52 :(
## 5: 43 0.66 :(
## 6: 37 0.041 *
## 7: 44 0.069 .
## 8: 42 0.11 :(
## 9: 41 0.19 :(
## 10: 38 0.089 .
## 11: 42 0.68 :(
## 12: 41 0.028 *
## 13: 43 0.00017 ***
## 14: 42 6e-06 ***
## 15: 39 5.3e-08 ***
## 16: 37 1.1e-07 ***
## [1] 39.2
## [1] 5.01
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 3 NA
## 2: 0.09375 0.049 12 0.72 :(
## 3: 0.15625 0.022 19 0.48 :(
## 4: 0.21875 0.021 20 0.69 :(
## 5: 0.28125 0.040 20 0.51 :(
## 6: 0.34375 0.085 21 0.22 :(
## 7: 0.40625 -0.013 21 0.86 :(
## 8: 0.46875 -0.040 19 0.48 :(
## 9: 0.53125 -0.100 17 0.13 :(
## 10: 0.59375 -0.076 22 0.27 :(
## 11: 0.65625 -0.085 21 0.25 :(
## 12: 0.71875 -0.150 24 0.0037 **
## 13: 0.78125 -0.100 24 0.017 *
## 14: 0.84375 -0.180 25 0.00068 ***
## 15: 0.90625 -0.120 25 1.2e-05 ***
## 16: 0.96875 -0.180 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 12 0.72 :(
## 2: 19 0.48 :(
## 3: 20 0.69 :(
## 4: 20 0.51 :(
## 5: 21 0.22 :(
## 6: 21 0.86 :(
## 7: 19 0.48 :(
## 8: 17 0.13 :(
## 9: 22 0.27 :(
## 10: 21 0.25 :(
## 11: 24 0.0037 **
## 12: 24 0.017 *
## 13: 25 0.00068 ***
## 14: 25 1.2e-05 ***
## 15: 25 1.3e-05 ***
## [1] 21
## [1] 3.53## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0940 8 0.71 :(
## 3: 0.15625 -0.0990 29 0.021 *
## 4: 0.21875 -0.0760 41 0.042 *
## 5: 0.28125 -0.0540 48 0.2 :(
## 6: 0.34375 -0.0400 50 0.22 :(
## 7: 0.40625 -0.0015 50 0.9 :(
## 8: 0.46875 -0.0022 54 1 :(
## 9: 0.53125 0.0400 52 0.17 :(
## 10: 0.59375 0.0063 51 0.82 :(
## 11: 0.65625 0.0220 52 0.79 :(
## 12: 0.71875 -0.0580 53 0.064 .
## 13: 0.78125 -0.0790 46 0.015 *
## 14: 0.84375 -0.0940 29 0.077 .
## 15: 0.90625 -0.0760 13 0.0012 **
## 16: 0.96875 -0.1100 6 0.031 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.71 :(
## 2: 29 0.021 *
## 3: 41 0.042 *
## 4: 48 0.2 :(
## 5: 50 0.22 :(
## 6: 50 0.9 :(
## 7: 54 1 :(
## 8: 52 0.17 :(
## 9: 51 0.82 :(
## 10: 52 0.79 :(
## 11: 53 0.064 .
## 12: 46 0.015 *
## 13: 29 0.077 .
## 14: 13 0.0012 **
## 15: 6 0.031 *
## [1] 38.8
## [1] 17.3## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.094 8 0.71 :(
## 3: 0.15625 -0.099 24 0.023 *
## 4: 0.21875 -0.066 25 0.067 .
## 5: 0.28125 -0.043 25 0.31 :(
## 6: 0.34375 -0.040 25 0.32 :(
## 7: 0.40625 0.040 24 0.4 :(
## 8: 0.46875 0.067 24 0.12 :(
## 9: 0.53125 0.110 23 0.021 *
## 10: 0.59375 0.120 22 0.043 *
## 11: 0.65625 0.029 22 0.52 :(
## 12: 0.71875 -0.040 21 0.094 .
## 13: 0.78125 -0.067 15 0.32 :(
## 14: 0.84375 NA 2 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.71 :(
## 2: 24 0.023 *
## 3: 25 0.067 .
## 4: 25 0.31 :(
## 5: 25 0.32 :(
## 6: 24 0.4 :(
## 7: 24 0.12 :(
## 8: 23 0.021 *
## 9: 22 0.043 *
## 10: 22 0.52 :(
## 11: 21 0.094 .
## 12: 15 0.32 :(
## [1] 21.5
## [1] 5.07## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 5 NA
## 4: 0.21875 -0.0045 16 0.41 :(
## 5: 0.28125 -0.0670 23 0.51 :(
## 6: 0.34375 -0.0580 24 0.3 :(
## 7: 0.40625 -0.0320 25 0.73 :(
## 8: 0.46875 -0.0400 25 0.5 :(
## 9: 0.53125 0.0220 25 0.69 :(
## 10: 0.59375 -0.0220 22 0.9 :(
## 11: 0.65625 0.0410 23 0.66 :(
## 12: 0.71875 0.0310 25 0.65 :(
## 13: 0.78125 -0.0670 25 0.13 :(
## 14: 0.84375 -0.0940 20 0.15 :(
## 15: 0.90625 NA 6 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 16 0.41 :(
## 2: 23 0.51 :(
## 3: 24 0.3 :(
## 4: 25 0.73 :(
## 5: 25 0.5 :(
## 6: 25 0.69 :(
## 7: 22 0.9 :(
## 8: 23 0.66 :(
## 9: 25 0.65 :(
## 10: 25 0.13 :(
## 11: 20 0.15 :(
## [1] 23
## [1] 2.83## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 NA 1 NA
## 8: 0.46875 -0.150 5 0.28 :(
## 9: 0.53125 -0.220 4 0.38 :(
## 10: 0.59375 -0.290 7 0.078 .
## 11: 0.65625 -0.130 7 0.35 :(
## 12: 0.71875 -0.260 7 0.047 *
## 13: 0.78125 -0.160 6 0.16 :(
## 14: 0.84375 -0.120 7 0.2 :(
## 15: 0.90625 -0.081 7 0.022 *
## 16: 0.96875 -0.110 6 0.031 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.28 :(
## 2: 4 0.38 :(
## 3: 7 0.078 .
## 4: 7 0.35 :(
## 5: 7 0.047 *
## 6: 6 0.16 :(
## 7: 7 0.2 :(
## 8: 7 0.022 *
## 9: 6 0.031 *
## [1] 6.22
## [1] 1.09## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 32 0.034 *
## 2: 0.09375 -0.0065 48 0.64 :(
## 3: 0.15625 -0.0970 51 0.0069 **
## 4: 0.21875 -0.0760 47 0.0011 **
## 5: 0.28125 -0.0670 46 0.1 :(
## 6: 0.34375 -0.1300 41 0.063 .
## 7: 0.40625 -0.1200 44 0.053 .
## 8: 0.46875 -0.1100 42 0.036 *
## 9: 0.53125 -0.1700 34 0.0079 **
## 10: 0.59375 -0.2400 37 0.00062 ***
## 11: 0.65625 -0.1100 40 0.12 :(
## 12: 0.71875 -0.1700 46 0.00063 ***
## 13: 0.78125 -0.1700 42 0.0042 **
## 14: 0.84375 -0.1700 46 9e-06 ***
## 15: 0.90625 -0.1600 53 1.9e-10 ***
## 16: 0.96875 -0.1400 55 9.4e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 32 0.034 *
## 2: 48 0.64 :(
## 3: 51 0.0069 **
## 4: 47 0.0011 **
## 5: 46 0.1 :(
## 6: 41 0.063 .
## 7: 44 0.053 .
## 8: 42 0.036 *
## 9: 34 0.0079 **
## 10: 37 0.00062 ***
## 11: 40 0.12 :(
## 12: 46 0.00063 ***
## 13: 42 0.0042 **
## 14: 46 9e-06 ***
## 15: 53 1.9e-10 ***
## 16: 55 9.4e-11 ***
## [1] 44
## [1] 6.4
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.054 10 0.36 :(
## 2: 0.09375 NA 10 NA
## 3: 0.15625 -0.110 9 0.18 :(
## 4: 0.21875 -0.150 5 0.099 .
## 5: 0.28125 -0.100 8 0.53 :(
## 6: 0.34375 -0.130 6 0.4 :(
## 7: 0.40625 -0.190 7 0.27 :(
## 8: 0.46875 -0.250 9 0.07 .
## 9: 0.53125 -0.210 7 0.2 :(
## 10: 0.59375 -0.380 6 0.058 .
## 11: 0.65625 -0.085 5 0.59 :(
## 12: 0.71875 -0.290 9 0.044 *
## 13: 0.78125 -0.250 7 0.15 :(
## 14: 0.84375 -0.130 7 0.2 :(
## 15: 0.90625 -0.085 9 0.008 **
## 16: 0.96875 -0.150 10 0.0053 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 10 0.36 :(
## 2: 9 0.18 :(
## 3: 5 0.099 .
## 4: 8 0.53 :(
## 5: 6 0.4 :(
## 6: 7 0.27 :(
## 7: 9 0.07 .
## 8: 7 0.2 :(
## 9: 6 0.058 .
## 10: 5 0.59 :(
## 11: 9 0.044 *
## 12: 7 0.15 :(
## 13: 7 0.2 :(
## 14: 9 0.008 **
## 15: 10 0.0053 **
## [1] 7.6
## [1] 1.68## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 19 0.001 **
## 2: 0.09375 -0.004 27 0.97 :(
## 3: 0.15625 -0.160 26 9.3e-05 ***
## 4: 0.21875 -0.150 24 0.00077 ***
## 5: 0.28125 -0.170 22 0.05 .
## 6: 0.34375 -0.170 19 0.14 :(
## 7: 0.40625 -0.190 22 0.025 *
## 8: 0.46875 -0.040 23 0.45 :(
## 9: 0.53125 -0.220 18 0.02 *
## 10: 0.59375 -0.270 20 0.0015 **
## 11: 0.65625 -0.160 22 0.079 .
## 12: 0.71875 -0.150 21 0.0059 **
## 13: 0.78125 -0.250 19 0.034 *
## 14: 0.84375 -0.220 24 0.00023 ***
## 15: 0.90625 -0.190 27 5.7e-06 ***
## 16: 0.96875 -0.150 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 19 0.001 **
## 2: 27 0.97 :(
## 3: 26 9.3e-05 ***
## 4: 24 0.00077 ***
## 5: 22 0.05 .
## 6: 19 0.14 :(
## 7: 22 0.025 *
## 8: 23 0.45 :(
## 9: 18 0.02 *
## 10: 20 0.0015 **
## 11: 22 0.079 .
## 12: 21 0.0059 **
## 13: 19 0.034 *
## 14: 24 0.00023 ***
## 15: 27 5.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 22.5
## [1] 3.1
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 3 NA
## 2: 0.09375 0.0650 11 0.56 :(
## 3: 0.15625 0.0360 16 0.42 :(
## 4: 0.21875 -0.0045 18 0.76 :(
## 5: 0.28125 0.0045 16 0.9 :(
## 6: 0.34375 -0.1300 16 0.58 :(
## 7: 0.40625 0.0580 15 0.51 :(
## 8: 0.46875 -0.1000 10 0.15 :(
## 9: 0.53125 -0.1000 9 0.72 :(
## 10: 0.59375 -0.1500 11 0.82 :(
## 11: 0.65625 0.0220 13 0.78 :(
## 12: 0.71875 -0.0400 16 0.36 :(
## 13: 0.78125 -0.0710 16 0.26 :(
## 14: 0.84375 -0.0820 15 0.07 .
## 15: 0.90625 -0.1600 17 0.00027 ***
## 16: 0.96875 -0.1300 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.56 :(
## 2: 16 0.42 :(
## 3: 18 0.76 :(
## 4: 16 0.9 :(
## 5: 16 0.58 :(
## 6: 15 0.51 :(
## 7: 10 0.15 :(
## 8: 9 0.72 :(
## 9: 11 0.82 :(
## 10: 13 0.78 :(
## 11: 16 0.36 :(
## 12: 16 0.26 :(
## 13: 15 0.07 .
## 14: 17 0.00027 ***
## 15: 18 0.00021 ***
## [1] 14.5
## [1] 2.92## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0005 38 0.78 :(
## 2: 0.09375 0.0970 43 0.01 *
## 3: 0.15625 0.0940 48 0.04 *
## 4: 0.21875 0.1600 50 0.0046 **
## 5: 0.28125 0.1500 49 0.015 *
## 6: 0.34375 0.0850 41 0.08 .
## 7: 0.40625 0.0220 47 0.77 :(
## 8: 0.46875 -0.0400 47 0.64 :(
## 9: 0.53125 0.0160 45 0.73 :(
## 10: 0.59375 -0.0370 46 0.6 :(
## 11: 0.65625 -0.0490 42 0.32 :(
## 12: 0.71875 -0.1500 41 0.00057 ***
## 13: 0.78125 -0.1400 53 0.00026 ***
## 14: 0.84375 -0.2600 52 1.4e-08 ***
## 15: 0.90625 -0.2400 42 1.7e-08 ***
## 16: 0.96875 -0.3300 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 38 0.78 :(
## 2: 43 0.01 *
## 3: 48 0.04 *
## 4: 50 0.0046 **
## 5: 49 0.015 *
## 6: 41 0.08 .
## 7: 47 0.77 :(
## 8: 47 0.64 :(
## 9: 45 0.73 :(
## 10: 46 0.6 :(
## 11: 42 0.32 :(
## 12: 41 0.00057 ***
## 13: 53 0.00026 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.7e-08 ***
## 16: 29 2.7e-06 ***
## [1] 44.6
## [1] 5.93
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.012 29 0.55 :(
## 2: 0.09375 0.080 29 0.016 *
## 3: 0.15625 0.068 28 0.25 :(
## 4: 0.21875 0.160 26 0.042 *
## 5: 0.28125 0.076 24 0.28 :(
## 6: 0.34375 0.013 21 0.78 :(
## 7: 0.40625 0.022 21 0.58 :(
## 8: 0.46875 0.100 25 0.39 :(
## 9: 0.53125 0.040 23 0.61 :(
## 10: 0.59375 -0.099 23 0.13 :(
## 11: 0.65625 -0.085 22 0.17 :(
## 12: 0.71875 -0.150 20 0.0092 **
## 13: 0.78125 -0.091 27 0.084 .
## 14: 0.84375 -0.270 25 7.5e-05 ***
## 15: 0.90625 -0.260 15 0.00071 ***
## 16: 0.96875 -0.330 2 0.5 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 29 0.55 :(
## 2: 29 0.016 *
## 3: 28 0.25 :(
## 4: 26 0.042 *
## 5: 24 0.28 :(
## 6: 21 0.78 :(
## 7: 21 0.58 :(
## 8: 25 0.39 :(
## 9: 23 0.61 :(
## 10: 23 0.13 :(
## 11: 22 0.17 :(
## 12: 20 0.0092 **
## 13: 27 0.084 .
## 14: 25 7.5e-05 ***
## 15: 15 0.00071 ***
## 16: 2 0.5 :(
## [1] 22.5
## [1] 6.58
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 9 0.53 :(
## 2: 0.09375 0.140 13 0.09 .
## 3: 0.15625 0.180 16 0.046 *
## 4: 0.21875 0.140 18 0.13 :(
## 5: 0.28125 0.150 17 0.079 .
## 6: 0.34375 0.085 14 0.31 :(
## 7: 0.40625 0.022 16 0.94 :(
## 8: 0.46875 -0.180 14 0.065 .
## 9: 0.53125 0.040 14 0.34 :(
## 10: 0.59375 0.085 14 0.9 :(
## 11: 0.65625 -0.013 13 0.89 :(
## 12: 0.71875 -0.150 15 0.24 :(
## 13: 0.78125 -0.220 17 0.0024 **
## 14: 0.84375 -0.220 17 0.00091 ***
## 15: 0.90625 -0.230 17 0.00032 ***
## 16: 0.96875 -0.330 17 0.00031 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.53 :(
## 2: 13 0.09 .
## 3: 16 0.046 *
## 4: 18 0.13 :(
## 5: 17 0.079 .
## 6: 14 0.31 :(
## 7: 16 0.94 :(
## 8: 14 0.065 .
## 9: 14 0.34 :(
## 10: 14 0.9 :(
## 11: 13 0.89 :(
## 12: 15 0.24 :(
## 13: 17 0.0024 **
## 14: 17 0.00091 ***
## 15: 17 0.00032 ***
## 16: 17 0.00031 ***
## [1] 15.1
## [1] 2.29
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 1 NA
## 3: 0.15625 NA 4 NA
## 4: 0.21875 0.210 6 0.21 :(
## 5: 0.28125 0.190 8 0.18 :(
## 6: 0.34375 0.410 6 0.09 .
## 7: 0.40625 -0.031 10 0.92 :(
## 8: 0.46875 0.066 8 0.73 :(
## 9: 0.53125 -0.120 8 0.23 :(
## 10: 0.59375 0.120 9 0.63 :(
## 11: 0.65625 0.130 7 0.93 :(
## 12: 0.71875 -0.280 6 0.031 *
## 13: 0.78125 -0.079 9 0.28 :(
## 14: 0.84375 -0.270 10 0.032 *
## 15: 0.90625 -0.190 10 0.0058 **
## 16: 0.96875 -0.340 10 0.002 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 6 0.21 :(
## 2: 8 0.18 :(
## 3: 6 0.09 .
## 4: 10 0.92 :(
## 5: 8 0.73 :(
## 6: 8 0.23 :(
## 7: 9 0.63 :(
## 8: 7 0.93 :(
## 9: 6 0.031 *
## 10: 9 0.28 :(
## 11: 10 0.032 *
## 12: 10 0.0058 **
## 13: 10 0.002 **
## [1] 8.23
## [1] 1.59## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.85425 -0.20543 0.02783 0.20243 0.70750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006664 0.018685 -0.357 0.721400
## timeNorm 0.016339 0.020930 0.781 0.435103
## obj.diff -0.094710 0.028659 -3.305 0.000969 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07620175)
##
## Null deviance: 143.99 on 1880 degrees of freedom
## Residual deviance: 143.11 on 1878 degrees of freedom
## AIC: 500.65
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78155 -0.13780 -0.01151 0.12280 0.83005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006795 0.014205 -0.478 0.632
## timeNorm 0.011968 0.020729 0.577 0.564
## obj.diff -0.205459 0.018132 -11.331 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0706054)
##
## Null deviance: 137.08 on 1814 degrees of freedom
## Residual deviance: 127.94 on 1812 degrees of freedom
## AIC: 344.82
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71153 -0.24157 0.00719 0.24129 0.65825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17431 0.01800 9.684 <2e-16 ***
## timeNorm 0.02107 0.02417 0.872 0.384
## obj.diff -0.46661 0.02329 -20.037 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1007759)
##
## Null deviance: 230.47 on 1880 degrees of freedom
## Residual deviance: 189.26 on 1878 degrees of freedom
## AIC: 1026.4
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3918129 0.4457102 -0.05543814 342 0.0017 **
## 2: 4.5 0.4954052 0.5624859 -0.05783222 171 0.0052 **
## 3: 7.5 0.4928989 0.5357049 -0.03693287 171 0.077 .
## 4: 10.5 0.5071011 0.5362058 -0.02578583 171 0.23 :(
## 5: 13.5 0.4519632 0.5133937 -0.05576376 171 0.0061 **
## 6: 16.5 0.4995823 0.5320036 -0.01539779 171 0.46 :(
## 7: 19.5 0.4803676 0.5358363 -0.04608428 171 0.025 *
## 8: 22.5 0.4527987 0.4961373 -0.03638516 171 0.091 .
## 9: 25.5 0.4536341 0.4868060 -0.02527202 171 0.27 :(
## 10: 28.5 0.4243943 0.4657574 -0.03934980 171 0.074 .
## time error.diff shapes
## 1: 1.5 -0.05543814 24
## 2: 4.5 -0.05783222 24
## 3: 7.5 -0.03693287 16
## 4: 10.5 -0.02578583 16
## 5: 13.5 -0.05576376 24
## 6: 16.5 -0.01539779 16
## 7: 19.5 -0.04608428 24
## 8: 22.5 -0.03638516 16
## 9: 25.5 -0.02527202 16
## 10: 28.5 -0.03934980 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2761905 0.3121174 -0.08025653 330 2.2e-05 ***
## 2: 4.5 0.5194805 0.6623901 -0.12246157 165 6.5e-13 ***
## 3: 7.5 0.4259740 0.5756691 -0.12748326 165 6.7e-13 ***
## 4: 10.5 0.4658009 0.6169890 -0.12563962 165 5.6e-14 ***
## 5: 13.5 0.4251082 0.5882784 -0.13475627 165 4.2e-16 ***
## 6: 16.5 0.4025974 0.5480044 -0.12850300 165 1.9e-12 ***
## 7: 19.5 0.4666667 0.5706900 -0.09391859 165 2.3e-08 ***
## 8: 22.5 0.4311688 0.5568448 -0.12173493 165 1.6e-10 ***
## 9: 25.5 0.4891775 0.5635905 -0.08515151 165 7.1e-08 ***
## 10: 28.5 0.4649351 0.5525507 -0.08873994 165 1.2e-07 ***
## time error.diff shapes
## 1: 1.5 -0.08025653 24
## 2: 4.5 -0.12246157 24
## 3: 7.5 -0.12748326 24
## 4: 10.5 -0.12563962 24
## 5: 13.5 -0.13475627 24
## 6: 16.5 -0.12850300 24
## 7: 19.5 -0.09391859 24
## 8: 22.5 -0.12173493 24
## 9: 25.5 -0.08515151 24
## 10: 28.5 -0.08873994 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3483709 0.3515160 -0.0257788254 342 0.26 :(
## 2: 4.5 0.5037594 0.6513076 -0.1432600132 171 4.6e-08 ***
## 3: 7.5 0.5037594 0.5682979 -0.0702473202 171 0.0057 **
## 4: 10.5 0.4970760 0.5388474 -0.0530333212 171 0.04 *
## 5: 13.5 0.4761905 0.5225795 -0.0457087630 171 0.099 .
## 6: 16.5 0.4820384 0.5042410 -0.0325739632 171 0.21 :(
## 7: 19.5 0.4185464 0.4415088 -0.0319575055 171 0.25 :(
## 8: 22.5 0.3918129 0.4078173 -0.0213488721 171 0.43 :(
## 9: 25.5 0.3851295 0.3856125 -0.0035008941 171 0.9 :(
## 10: 28.5 0.3792815 0.3513216 -0.0006985616 171 0.98 :(
## time error.diff shapes
## 1: 1.5 -0.0257788254 16
## 2: 4.5 -0.1432600132 24
## 3: 7.5 -0.0702473202 24
## 4: 10.5 -0.0530333212 24
## 5: 13.5 -0.0457087630 16
## 6: 16.5 -0.0325739632 16
## 7: 19.5 -0.0319575055 16
## 8: 22.5 -0.0213488721 16
## 9: 25.5 -0.0035008941 16
## 10: 28.5 -0.0006985616 16
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7569 -0.2283 0.1044 0.1775 0.6607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12790 0.02644 4.837 1.5e-06 ***
## timeNorm 0.03074 0.03011 1.021 0.307
## obj.diff -0.37938 0.03148 -12.051 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0966408)
##
## Null deviance: 125.39 on 1154 degrees of freedom
## Residual deviance: 111.33 on 1152 degrees of freedom
## AIC: 583.79
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78332 -0.20655 0.01988 0.20932 0.76046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04636 0.01554 2.982 0.00289 **
## timeNorm 0.03107 0.02013 1.544 0.12281
## obj.diff -0.26193 0.02129 -12.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08617747)
##
## Null deviance: 211.91 on 2309 degrees of freedom
## Residual deviance: 198.81 on 2307 degrees of freedom
## AIC: 897.88
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73041 -0.19642 -0.06578 0.20222 0.76122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06693 0.01436 4.660 3.36e-06 ***
## timeNorm 0.02111 0.01996 1.058 0.29
## obj.diff -0.25660 0.02270 -11.303 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07766279)
##
## Null deviance: 173.88 on 2111 degrees of freedom
## Residual deviance: 163.79 on 2109 degrees of freedom
## AIC: 601.63
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4503401 0.5078590 -0.06911867 210 0.011 *
## 2: 4.5 0.6217687 0.7854497 -0.12506562 105 4e-08 ***
## 3: 7.5 0.6231293 0.7571408 -0.11442931 105 2.3e-06 ***
## 4: 10.5 0.5986395 0.7378391 -0.11609559 105 5.6e-06 ***
## 5: 13.5 0.5768707 0.7194038 -0.11978956 105 1.1e-06 ***
## 6: 16.5 0.5496599 0.6928809 -0.11831475 105 7.5e-06 ***
## 7: 19.5 0.5496599 0.6519999 -0.08328775 105 0.0045 **
## 8: 22.5 0.5469388 0.6672242 -0.10170834 105 9.6e-05 ***
## 9: 25.5 0.5306122 0.6374488 -0.09280897 105 0.00011 ***
## 10: 28.5 0.5986395 0.6321308 -0.04493309 105 0.046 *
## time error.diff shapes
## 1: 1.5 -0.06911867 24
## 2: 4.5 -0.12506562 24
## 3: 7.5 -0.11442931 24
## 4: 10.5 -0.11609559 24
## 5: 13.5 -0.11978956 24
## 6: 16.5 -0.11831475 24
## 7: 19.5 -0.08328775 24
## 8: 22.5 -0.10170834 24
## 9: 25.5 -0.09280897 24
## 10: 28.5 -0.04493309 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3363946 0.3833571 -0.06472476 420 0.00088 ***
## 2: 4.5 0.5156463 0.6644303 -0.13652906 210 7.9e-13 ***
## 3: 7.5 0.4646259 0.5546648 -0.08926828 210 2.4e-06 ***
## 4: 10.5 0.5142857 0.5856428 -0.08276584 210 4e-05 ***
## 5: 13.5 0.4680272 0.5711545 -0.09916733 210 1.5e-06 ***
## 6: 16.5 0.4884354 0.5645710 -0.08021078 210 0.00025 ***
## 7: 19.5 0.4925170 0.5763408 -0.07570676 210 4.2e-05 ***
## 8: 22.5 0.4278912 0.5129160 -0.09031318 210 2.9e-05 ***
## 9: 25.5 0.4829932 0.5222933 -0.04941546 210 0.038 *
## 10: 28.5 0.4408163 0.5011792 -0.06649074 210 0.0011 **
## time error.diff shapes
## 1: 1.5 -0.06472476 24
## 2: 4.5 -0.13652906 24
## 3: 7.5 -0.08926828 24
## 4: 10.5 -0.08276584 24
## 5: 13.5 -0.09916733 24
## 6: 16.5 -0.08021078 24
## 7: 19.5 -0.07570676 24
## 8: 22.5 -0.09031318 24
## 9: 25.5 -0.04941546 24
## 10: 28.5 -0.06649074 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2823661 0.2812232 -0.034155981 384 0.054 .
## 2: 4.5 0.4322917 0.4940130 -0.065713509 192 0.0054 **
## 3: 7.5 0.4047619 0.4572421 -0.053881101 192 0.016 *
## 4: 10.5 0.4047619 0.4436417 -0.041433663 192 0.054 .
## 5: 13.5 0.3645833 0.4100912 -0.052717074 192 0.014 *
## 6: 16.5 0.3854167 0.3974278 -0.017335375 192 0.39 :(
## 7: 19.5 0.3623512 0.3739496 -0.026088074 192 0.22 :(
## 8: 22.5 0.3556548 0.3577330 -0.012479126 192 0.56 :(
## 9: 25.5 0.3489583 0.3414701 -0.002661268 192 0.91 :(
## 10: 28.5 0.3058036 0.3086979 -0.022918440 192 0.21 :(
## time error.diff shapes
## 1: 1.5 -0.034155981 16
## 2: 4.5 -0.065713509 24
## 3: 7.5 -0.053881101 24
## 4: 10.5 -0.041433663 16
## 5: 13.5 -0.052717074 24
## 6: 16.5 -0.017335375 16
## 7: 19.5 -0.026088074 16
## 8: 22.5 -0.012479126 16
## 9: 25.5 -0.002661268 16
## 10: 28.5 -0.022918440 16
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78307 -0.20030 0.09965 0.20287 0.56643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.22999 0.11949 -1.925 0.0555 .
## timeNorm -0.03598 0.06998 -0.514 0.6077
## obj.diff 0.08283 0.15050 0.550 0.5826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1038657)
##
## Null deviance: 23.736 on 230 degrees of freedom
## Residual deviance: 23.681 on 228 degrees of freedom
## AIC: 137.39
##
## Number of Fisher Scoring iterations: 2## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5374150 0.7170177 -0.17337940 42 0.011 *
## 2: 4.5 0.6122449 0.7946114 -0.14180958 21 0.0063 **
## 3: 7.5 0.6326531 0.7649789 -0.07966557 21 0.038 *
## 4: 10.5 0.6394558 0.7869246 -0.09982316 21 0.009 **
## 5: 13.5 0.6190476 0.8120284 -0.09702938 21 0.013 *
## 6: 16.5 0.5102041 0.7887369 -0.26023913 21 0.0049 **
## 7: 19.5 0.5442177 0.7250289 -0.16961596 21 0.05 .
## 8: 22.5 0.6462585 0.7637626 -0.03896849 21 0.49 :(
## 9: 25.5 0.5578231 0.8157609 -0.26561302 21 0.00072 ***
## 10: 28.5 0.5986395 0.7674702 -0.09317669 21 0.06 .
## time error.diff shapes
## 1: 1.5 -0.17337940 24
## 2: 4.5 -0.14180958 24
## 3: 7.5 -0.07966557 24
## 4: 10.5 -0.09982316 24
## 5: 13.5 -0.09702938 24
## 6: 16.5 -0.26023913 24
## 7: 19.5 -0.16961596 16
## 8: 22.5 -0.03896849 16
## 9: 25.5 -0.26561302 24
## 10: 28.5 -0.09317669 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79768 -0.22927 0.04496 0.19205 0.68904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04646 0.03146 -1.477 0.140
## timeNorm 0.01656 0.03145 0.527 0.599
## obj.diff -0.01154 0.04892 -0.236 0.814
##
## (Dispersion parameter for gaussian family taken to be 0.07542452)
##
## Null deviance: 62.024 on 824 degrees of freedom
## Residual deviance: 61.999 on 822 degrees of freedom
## AIC: 213.93
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4219048 0.4703926 -0.04882302 150 0.077 .
## 2: 4.5 0.5314286 0.6181213 -0.07545117 75 0.02 *
## 3: 7.5 0.5028571 0.5405554 -0.02915190 75 0.35 :(
## 4: 10.5 0.5333333 0.5682867 -0.03243828 75 0.34 :(
## 5: 13.5 0.5200000 0.5516441 -0.02674953 75 0.43 :(
## 6: 16.5 0.5428571 0.5685882 -0.02122707 75 0.62 :(
## 7: 19.5 0.5447619 0.5794923 -0.02965771 75 0.39 :(
## 8: 22.5 0.4380952 0.5231952 -0.09195474 75 0.015 *
## 9: 25.5 0.4819048 0.5079792 -0.03043348 75 0.41 :(
## 10: 28.5 0.4819048 0.5148979 -0.03878182 75 0.31 :(
## time error.diff shapes
## 1: 1.5 -0.04882302 16
## 2: 4.5 -0.07545117 24
## 3: 7.5 -0.02915190 16
## 4: 10.5 -0.03243828 16
## 5: 13.5 -0.02674953 16
## 6: 16.5 -0.02122707 16
## 7: 19.5 -0.02965771 16
## 8: 22.5 -0.09195474 24
## 9: 25.5 -0.03043348 16
## 10: 28.5 -0.03878182 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80335 -0.18155 -0.01888 0.19399 0.72918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06141 0.02493 -2.463 0.0140 *
## timeNorm 0.03220 0.02896 1.112 0.2664
## obj.diff 0.08952 0.04587 1.952 0.0513 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06387276)
##
## Null deviance: 52.815 on 824 degrees of freedom
## Residual deviance: 52.503 on 822 degrees of freedom
## AIC: 76.782
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3209524 0.3450617 -0.03697026 150 0.13 :(
## 2: 4.5 0.4266667 0.4418554 -0.01357897 75 0.61 :(
## 3: 7.5 0.4438095 0.4666577 -0.02092597 75 0.44 :(
## 4: 10.5 0.4438095 0.4339237 0.01170254 75 0.74 :(
## 5: 13.5 0.3371429 0.3915256 -0.05898709 75 0.041 *
## 6: 16.5 0.4533333 0.4235337 0.02549517 75 0.27 :(
## 7: 19.5 0.3980952 0.4392064 -0.03752952 75 0.23 :(
## 8: 22.5 0.4133333 0.3941442 0.01243771 75 0.56 :(
## 9: 25.5 0.3961905 0.3735255 0.02575106 75 0.45 :(
## 10: 28.5 0.3180952 0.3321373 -0.01719639 75 0.56 :(
## time error.diff shapes
## 1: 1.5 -0.03697026 16
## 2: 4.5 -0.01357897 16
## 3: 7.5 -0.02092597 16
## 4: 10.5 0.01170254 16
## 5: 13.5 -0.05898709 24
## 6: 16.5 0.02549517 16
## 7: 19.5 -0.03752952 16
## 8: 22.5 0.01243771 16
## 9: 25.5 0.02575106 16
## 10: 28.5 -0.01719639 16
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80489 -0.19715 0.04686 0.12205 0.71041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10731 0.02825 3.799 0.000161 ***
## timeNorm -0.01236 0.03779 -0.327 0.743602
## obj.diff -0.28137 0.03440 -8.179 1.76e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07744331)
##
## Null deviance: 51.071 on 593 degrees of freedom
## Residual deviance: 45.769 on 591 degrees of freedom
## AIC: 171.12
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4060847 0.3647578 0.01440955 108 0.66 :(
## 2: 4.5 0.6481481 0.7356615 -0.10467396 54 0.0024 **
## 3: 7.5 0.5925926 0.7147027 -0.11933066 54 0.0012 **
## 4: 10.5 0.5767196 0.7227008 -0.12067509 54 0.00057 ***
## 5: 13.5 0.5343915 0.6812848 -0.12776766 54 4.4e-06 ***
## 6: 16.5 0.5291005 0.6163897 -0.09974710 54 0.011 *
## 7: 19.5 0.5449735 0.6057373 -0.09050373 54 0.1 :(
## 8: 22.5 0.5449735 0.6322030 -0.11182543 54 0.0041 **
## 9: 25.5 0.5000000 0.5850422 -0.09763863 54 0.00011 ***
## 10: 28.5 0.5846561 0.5894469 -0.04923471 54 0.17 :(
## time error.diff shapes
## 1: 1.5 0.01440955 16
## 2: 4.5 -0.10467396 24
## 3: 7.5 -0.11933066 24
## 4: 10.5 -0.12067509 24
## 5: 13.5 -0.12776766 24
## 6: 16.5 -0.09974710 24
## 7: 19.5 -0.09050373 16
## 8: 22.5 -0.11182543 24
## 9: 25.5 -0.09763863 24
## 10: 28.5 -0.04923471 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7546 -0.1203 -0.0149 0.1332 0.8520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04967 0.01924 -2.582 0.00998 **
## timeNorm 0.02696 0.02862 0.942 0.34649
## obj.diff -0.19284 0.02517 -7.662 4.78e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06551003)
##
## Null deviance: 62.028 on 890 degrees of freedom
## Residual deviance: 58.173 on 888 degrees of freedom
## AIC: 105.08
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2292769 0.3038003 -0.10333005 162 1.7e-05 ***
## 2: 4.5 0.4550265 0.6435586 -0.15826125 81 1.4e-09 ***
## 3: 7.5 0.3580247 0.5229068 -0.14268518 81 1.3e-07 ***
## 4: 10.5 0.4373898 0.6031879 -0.13652264 81 8e-10 ***
## 5: 13.5 0.3932981 0.5733030 -0.15553550 81 3e-09 ***
## 6: 16.5 0.3544974 0.5345376 -0.15396096 81 1.5e-09 ***
## 7: 19.5 0.4744268 0.5914733 -0.09274510 81 7.1e-06 ***
## 8: 22.5 0.3615520 0.5257793 -0.14843431 81 6.3e-07 ***
## 9: 25.5 0.5061728 0.5797558 -0.08411956 81 7e-04 ***
## 10: 28.5 0.4320988 0.5619786 -0.10644662 81 4.3e-07 ***
## time error.diff shapes
## 1: 1.5 -0.10333005 24
## 2: 4.5 -0.15826125 24
## 3: 7.5 -0.14268518 24
## 4: 10.5 -0.13652264 24
## 5: 13.5 -0.15553550 24
## 6: 16.5 -0.15396096 24
## 7: 19.5 -0.09274510 24
## 8: 22.5 -0.14843431 24
## 9: 25.5 -0.08411956 24
## 10: 28.5 -0.10644662 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6201 -0.1032 -0.0058 0.1318 0.8614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03880 0.02941 -1.319 0.188
## timeNorm 0.02457 0.04594 0.535 0.593
## obj.diff -0.20012 0.03979 -5.030 8.11e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06312685)
##
## Null deviance: 22.242 on 329 degrees of freedom
## Residual deviance: 20.642 on 327 degrees of freedom
## AIC: 29.825
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.1690476 0.2398207 -0.07330477 60 5.5e-06 ***
## 2: 4.5 0.4619048 0.5813467 -0.09880129 30 0.004 **
## 3: 7.5 0.3095238 0.4678668 -0.11016732 30 1.2e-05 ***
## 4: 10.5 0.3428571 0.4639707 -0.10959939 30 0.0024 **
## 5: 13.5 0.3142857 0.4613004 -0.11754338 30 0.00046 ***
## 6: 16.5 0.3047619 0.4612711 -0.11813934 30 0.00095 ***
## 7: 19.5 0.3047619 0.4514902 -0.10794597 30 0.00021 ***
## 8: 22.5 0.4142857 0.5050770 -0.08925231 30 0.004 **
## 9: 25.5 0.4238095 0.4813309 -0.06206745 30 0.043 *
## 10: 28.5 0.3380952 0.4606821 -0.10198710 30 0.0081 **
## time error.diff shapes
## 1: 1.5 -0.07330477 24
## 2: 4.5 -0.09880129 24
## 3: 7.5 -0.11016732 24
## 4: 10.5 -0.10959939 24
## 5: 13.5 -0.11754338 24
## 6: 16.5 -0.11813934 24
## 7: 19.5 -0.10794597 24
## 8: 22.5 -0.08925231 24
## 9: 25.5 -0.06206745 24
## 10: 28.5 -0.10198710 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6906 -0.2872 0.1618 0.2406 0.4382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.30476 0.06531 4.666 4.48e-06 ***
## timeNorm 0.08868 0.06179 1.435 0.152
## obj.diff -0.68200 0.07366 -9.259 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1146889)
##
## Null deviance: 48.122 on 329 degrees of freedom
## Residual deviance: 37.503 on 327 degrees of freedom
## AIC: 226.86
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4690476 0.6190299 -0.155433290 60 0.0022 **
## 2: 4.5 0.5809524 0.8686553 -0.258174644 30 6.3e-05 ***
## 3: 7.5 0.6714286 0.8280425 -0.118687676 30 0.011 *
## 4: 10.5 0.6095238 0.7307282 -0.116673908 30 0.064 .
## 5: 13.5 0.6238095 0.7231805 -0.114980427 30 0.31 :(
## 6: 16.5 0.6142857 0.7634659 -0.122280354 30 0.012 *
## 7: 19.5 0.5619048 0.6841523 -0.079833137 30 0.24 :(
## 8: 22.5 0.4809524 0.6626856 -0.152304784 30 0.0071 **
## 9: 25.5 0.5666667 0.6069621 -0.027205125 30 0.79 :(
## 10: 28.5 0.6238095 0.6142242 -0.008382334 30 0.84 :(
## time error.diff shapes
## 1: 1.5 -0.155433290 24
## 2: 4.5 -0.258174644 24
## 3: 7.5 -0.118687676 24
## 4: 10.5 -0.116673908 16
## 5: 13.5 -0.114980427 16
## 6: 16.5 -0.122280354 24
## 7: 19.5 -0.079833137 16
## 8: 22.5 -0.152304784 24
## 9: 25.5 -0.027205125 16
## 10: 28.5 -0.008382334 16## Warning: Removed 1 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68259 -0.32180 0.07167 0.25551 0.56027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25839 0.03624 7.129 2.96e-12 ***
## timeNorm 0.01121 0.04515 0.248 0.804
## obj.diff -0.56174 0.04604 -12.201 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1114018)
##
## Null deviance: 82.564 on 593 degrees of freedom
## Residual deviance: 65.838 on 591 degrees of freedom
## AIC: 387.09
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3783069 0.3818097 -0.001249940 108 0.99 :(
## 2: 4.5 0.5846561 0.7600559 -0.180788400 54 8.6e-05 ***
## 3: 7.5 0.5714286 0.6218983 -0.064736939 54 0.17 :(
## 4: 10.5 0.6031746 0.5834307 0.001819149 54 0.95 :(
## 5: 13.5 0.5079365 0.5950296 -0.082973359 54 0.084 .
## 6: 16.5 0.6137566 0.6040416 -0.001443315 54 0.98 :(
## 7: 19.5 0.4470899 0.5492649 -0.116439771 54 0.039 *
## 8: 22.5 0.5132275 0.4793441 0.031002683 54 0.49 :(
## 9: 25.5 0.4497354 0.4559805 0.001828617 54 0.99 :(
## 10: 28.5 0.3968254 0.3909264 -0.008169040 54 0.84 :(
## time error.diff shapes
## 1: 1.5 -0.001249940 16
## 2: 4.5 -0.180788400 24
## 3: 7.5 -0.064736939 16
## 4: 10.5 0.001819149 16
## 5: 13.5 -0.082973359 16
## 6: 16.5 -0.001443315 16
## 7: 19.5 -0.116439771 24
## 8: 22.5 0.031002683 16
## 9: 25.5 0.001828617 16
## 10: 28.5 -0.008169040 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6716 -0.2008 -0.1021 0.2271 0.7066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.141067 0.022046 6.399 2.45e-10 ***
## timeNorm -0.001129 0.031530 -0.036 0.971
## obj.diff -0.402199 0.033874 -11.873 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08679706)
##
## Null deviance: 95.199 on 956 degrees of freedom
## Residual deviance: 82.804 on 954 degrees of freedom
## AIC: 381.76
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2881773 0.2404667 -0.000248799 174 0.99 :(
## 2: 4.5 0.4269294 0.5088613 -0.088531848 87 0.023 *
## 3: 7.5 0.4039409 0.4454615 -0.051697336 87 0.18 :(
## 4: 10.5 0.3924466 0.4450093 -0.054416649 87 0.06 .
## 5: 13.5 0.4055829 0.4084377 -0.004151434 87 0.93 :(
## 6: 16.5 0.3546798 0.3529078 -0.010002837 87 0.73 :(
## 7: 19.5 0.3513957 0.2909555 0.032337112 87 0.39 :(
## 8: 22.5 0.2857143 0.2755358 -0.005896073 87 0.87 :(
## 9: 25.5 0.2824302 0.2656083 -0.010305166 87 0.75 :(
## 10: 28.5 0.2840722 0.2360832 0.004864396 87 0.94 :(
## time error.diff shapes
## 1: 1.5 -0.000248799 16
## 2: 4.5 -0.088531848 24
## 3: 7.5 -0.051697336 16
## 4: 10.5 -0.054416649 16
## 5: 13.5 -0.004151434 16
## 6: 16.5 -0.010002837 16
## 7: 19.5 0.032337112 16
## 8: 22.5 -0.005896073 16
## 9: 25.5 -0.010305166 16
## 10: 28.5 0.004864396 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.88650 -0.19474 0.02121 0.20015 0.73863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02572 0.01258 -2.044 0.0411 *
## est.confidence.norm -0.04321 0.02231 -1.937 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07647613)
##
## Null deviance: 143.99 on 1880 degrees of freedom
## Residual deviance: 143.70 on 1879 degrees of freedom
## AIC: 506.41
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.87482 -0.09574 0.00360 0.07584 0.92532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10317 0.01295 -7.968 2.81e-15 ***
## est.confidence.norm -0.01433 0.02181 -0.657 0.511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07559423)
##
## Null deviance: 137.08 on 1814 degrees of freedom
## Residual deviance: 137.05 on 1813 degrees of freedom
## AIC: 467.73
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.96055 -0.18551 -0.02757 0.20982 0.87052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.031481 0.016416 -1.918 0.0553 .
## est.confidence.norm 0.001105 0.028651 0.039 0.9692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1226532)
##
## Null deviance: 230.47 on 1880 degrees of freedom
## Residual deviance: 230.47 on 1879 degrees of freedom
## AIC: 1395
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1633.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7925 -0.5834 -0.0572 0.5586 4.2448
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01823 0.1350
## Residual 0.07577 0.2753
## Number of obs: 5577, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04362 0.01956 77.00000 -2.230 0.0286 *
## est.confidence.norm -0.03058 0.01486 5560.00000 -2.058 0.0397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.378
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -162
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5725 -0.6690 0.0186 0.6541 3.3451
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02822 0.1680
## Residual 0.04882 0.2209
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03875 0.02664 97.80000 -1.455 0.149
## est.confidence.norm -0.01640 0.02826 1731.70000 -0.580 0.562
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.516
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 315.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0734 -0.4939 0.0180 0.4206 3.9205
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01030 0.1015
## Residual 0.06558 0.2561
## Number of obs: 1815, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11702 0.02156 146.20000 -5.428 2.31e-07 ***
## est.confidence.norm 0.01259 0.03019 742.10000 0.417 0.677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.721
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 998.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5425 -0.6144 -0.0593 0.5775 3.2918
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.03255 0.1804
## Residual 0.09176 0.3029
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08898 0.03103 118.30000 -2.867 0.00490 **
## est.confidence.norm 0.11638 0.03715 1557.40000 3.133 0.00176 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.597CONFIDENCE SCALE APPROACH
!!!!Ici commence l’approche basée sur l’échelle de confiance!!!!
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with zeroes## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with zeroes
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.150 47 1.8e-05 ***
## 2: 0.09375 0.140 54 5.2e-06 ***
## 3: 0.15625 0.140 58 1.3e-05 ***
## 4: 0.21875 0.100 58 7.1e-05 ***
## 5: 0.28125 0.110 57 1e-05 ***
## 6: 0.34375 0.090 58 0.0014 **
## 7: 0.40625 0.048 58 0.068 .
## 8: 0.46875 0.041 58 0.021 *
## 9: 0.53125 -0.031 58 0.034 *
## 10: 0.59375 -0.052 58 0.018 *
## 11: 0.65625 -0.071 58 0.01 *
## 12: 0.71875 -0.140 58 1.3e-06 ***
## 13: 0.78125 -0.170 58 6.7e-09 ***
## 14: 0.84375 -0.220 58 6.1e-09 ***
## 15: 0.90625 -0.230 57 2.1e-10 ***
## 16: 0.96875 -0.190 55 6.8e-09 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 47 1.8e-05 ***
## 2: 54 5.2e-06 ***
## 3: 58 1.3e-05 ***
## 4: 58 7.1e-05 ***
## 5: 57 1e-05 ***
## 6: 58 0.0014 **
## 7: 58 0.068 .
## 8: 58 0.021 *
## 9: 58 0.034 *
## 10: 58 0.018 *
## 11: 58 0.01 *
## 12: 58 1.3e-06 ***
## 13: 58 6.7e-09 ***
## 14: 58 6.1e-09 ***
## 15: 57 2.1e-10 ***
## 16: 55 6.8e-09 ***
## [1] 56.8
## [1] 2.86
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.090 32 0.0024 **
## 2: 0.09375 0.110 34 0.0021 **
## 3: 0.15625 0.100 42 0.0076 **
## 4: 0.21875 0.110 40 0.0042 **
## 5: 0.28125 0.110 38 0.0044 **
## 6: 0.34375 0.110 37 0.0043 **
## 7: 0.40625 0.056 36 0.037 *
## 8: 0.46875 0.056 38 0.039 *
## 9: 0.53125 0.026 40 0.5 :(
## 10: 0.59375 -0.027 39 0.45 :(
## 11: 0.65625 -0.023 35 0.41 :(
## 12: 0.71875 -0.150 37 0.00038 ***
## 13: 0.78125 -0.170 37 0.00037 ***
## 14: 0.84375 -0.250 29 3e-05 ***
## 15: 0.90625 -0.230 22 0.00031 ***
## 16: 0.96875 -0.100 11 0.036 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 32 0.0024 **
## 2: 34 0.0021 **
## 3: 42 0.0076 **
## 4: 40 0.0042 **
## 5: 38 0.0044 **
## 6: 37 0.0043 **
## 7: 36 0.037 *
## 8: 38 0.039 *
## 9: 40 0.5 :(
## 10: 39 0.45 :(
## 11: 35 0.41 :(
## 12: 37 0.00038 ***
## 13: 37 0.00037 ***
## 14: 29 3e-05 ***
## 15: 22 0.00031 ***
## 16: 11 0.036 *
## [1] 34.2
## [1] 7.86
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0860 24 0.19 :(
## 2: 0.09375 0.1600 33 0.00098 ***
## 3: 0.15625 0.0940 39 0.019 *
## 4: 0.21875 0.0520 43 0.089 .
## 5: 0.28125 0.1200 43 0.025 *
## 6: 0.34375 0.0650 37 0.23 :(
## 7: 0.40625 0.0028 44 0.97 :(
## 8: 0.46875 0.0012 42 1 :(
## 9: 0.53125 -0.0480 41 0.0078 **
## 10: 0.59375 -0.0940 38 0.012 *
## 11: 0.65625 -0.1600 42 0.00054 ***
## 12: 0.71875 -0.1600 41 0.00073 ***
## 13: 0.78125 -0.1800 43 5.9e-06 ***
## 14: 0.84375 -0.2400 42 4.4e-07 ***
## 15: 0.90625 -0.2600 39 2.5e-07 ***
## 16: 0.96875 -0.2200 37 1.2e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 24 0.19 :(
## 2: 33 0.00098 ***
## 3: 39 0.019 *
## 4: 43 0.089 .
## 5: 43 0.025 *
## 6: 37 0.23 :(
## 7: 44 0.97 :(
## 8: 42 1 :(
## 9: 41 0.0078 **
## 10: 38 0.012 *
## 11: 42 0.00054 ***
## 12: 41 0.00073 ***
## 13: 43 5.9e-06 ***
## 14: 42 4.4e-07 ***
## 15: 39 2.5e-07 ***
## 16: 37 1.2e-05 ***
## [1] 39.2
## [1] 5.01
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 3 NA
## 2: 0.09375 0.160 12 0.053 .
## 3: 0.15625 0.270 19 0.0011 **
## 4: 0.21875 0.160 20 0.0081 **
## 5: 0.28125 0.094 20 0.14 :(
## 6: 0.34375 0.160 21 0.033 *
## 7: 0.40625 0.094 21 0.091 .
## 8: 0.46875 0.069 19 0.086 .
## 9: 0.53125 -0.031 17 0.026 *
## 10: 0.59375 -0.077 22 0.11 :(
## 11: 0.65625 -0.160 21 0.26 :(
## 12: 0.71875 -0.200 24 0.0013 **
## 13: 0.78125 -0.240 24 0.00026 ***
## 14: 0.84375 -0.220 25 2e-04 ***
## 15: 0.90625 -0.210 25 0.00015 ***
## 16: 0.96875 -0.280 25 1.5e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 12 0.053 .
## 2: 19 0.0011 **
## 3: 20 0.0081 **
## 4: 20 0.14 :(
## 5: 21 0.033 *
## 6: 21 0.091 .
## 7: 19 0.086 .
## 8: 17 0.026 *
## 9: 22 0.11 :(
## 10: 21 0.26 :(
## 11: 24 0.0013 **
## 12: 24 0.00026 ***
## 13: 25 2e-04 ***
## 14: 25 0.00015 ***
## 15: 25 1.5e-05 ***
## [1] 21
## [1] 3.53## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.160 8 0.29 :(
## 3: 0.15625 0.094 29 0.22 :(
## 4: 0.21875 0.056 41 0.063 .
## 5: 0.28125 0.085 48 0.01 *
## 6: 0.34375 0.069 50 0.034 *
## 7: 0.40625 0.069 50 0.086 .
## 8: 0.46875 0.044 54 0.03 *
## 9: 0.53125 0.019 52 0.66 :(
## 10: 0.59375 -0.019 51 0.65 :(
## 11: 0.65625 -0.040 52 0.11 :(
## 12: 0.71875 -0.069 53 0.0037 **
## 13: 0.78125 -0.110 46 0.00082 ***
## 14: 0.84375 -0.170 29 0.0015 **
## 15: 0.90625 -0.180 13 0.056 .
## 16: 0.96875 -0.270 6 0.052 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.29 :(
## 2: 29 0.22 :(
## 3: 41 0.063 .
## 4: 48 0.01 *
## 5: 50 0.034 *
## 6: 50 0.086 .
## 7: 54 0.03 *
## 8: 52 0.66 :(
## 9: 51 0.65 :(
## 10: 52 0.11 :(
## 11: 53 0.0037 **
## 12: 46 0.00082 ***
## 13: 29 0.0015 **
## 14: 13 0.056 .
## 15: 6 0.052 .
## [1] 38.8
## [1] 17.3## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.1600 8 0.29 :(
## 3: 0.15625 0.0690 24 0.45 :(
## 4: 0.21875 0.0560 25 0.15 :(
## 5: 0.28125 0.0690 25 0.071 .
## 6: 0.34375 0.0560 25 0.089 .
## 7: 0.40625 0.0810 24 0.098 .
## 8: 0.46875 0.0940 24 0.031 *
## 9: 0.53125 0.0680 23 0.21 :(
## 10: 0.59375 0.0560 22 0.28 :(
## 11: 0.65625 -0.0062 22 1 :(
## 12: 0.71875 -0.0690 21 0.11 :(
## 13: 0.78125 -0.0650 15 0.081 .
## 14: 0.84375 NA 2 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.29 :(
## 2: 24 0.45 :(
## 3: 25 0.15 :(
## 4: 25 0.071 .
## 5: 25 0.089 .
## 6: 24 0.098 .
## 7: 24 0.031 *
## 8: 23 0.21 :(
## 9: 22 0.28 :(
## 10: 22 1 :(
## 11: 21 0.11 :(
## 12: 15 0.081 .
## [1] 21.5
## [1] 5.07## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.290 5 0.28 :(
## 4: 0.21875 0.048 16 0.22 :(
## 5: 0.28125 0.120 23 0.07 .
## 6: 0.34375 0.069 24 0.26 :(
## 7: 0.40625 0.054 25 0.45 :(
## 8: 0.46875 0.031 25 0.25 :(
## 9: 0.53125 -0.028 25 0.63 :(
## 10: 0.59375 -0.069 22 0.23 :(
## 11: 0.65625 -0.130 23 0.019 *
## 12: 0.71875 -0.069 25 0.084 .
## 13: 0.78125 -0.120 25 0.013 *
## 14: 0.84375 -0.170 20 0.024 *
## 15: 0.90625 -0.170 6 0.14 :(
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.28 :(
## 2: 16 0.22 :(
## 3: 23 0.07 .
## 4: 24 0.26 :(
## 5: 25 0.45 :(
## 6: 25 0.25 :(
## 7: 25 0.63 :(
## 8: 22 0.23 :(
## 9: 23 0.019 *
## 10: 25 0.084 .
## 11: 25 0.013 *
## 12: 20 0.024 *
## 13: 6 0.14 :(
## [1] 20.3
## [1] 7.06## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 NA 1 NA
## 8: 0.46875 0.031 5 0.58 :(
## 9: 0.53125 NA 4 NA
## 10: 0.59375 -0.050 7 0.1 :(
## 11: 0.65625 -0.019 7 0.93 :(
## 12: 0.71875 -0.120 7 0.15 :(
## 13: 0.78125 -0.130 6 0.14 :(
## 14: 0.84375 -0.170 7 0.11 :(
## 15: 0.90625 -0.200 7 0.27 :(
## 16: 0.96875 -0.270 6 0.052 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.58 :(
## 2: 7 0.1 :(
## 3: 7 0.93 :(
## 4: 7 0.15 :(
## 5: 6 0.14 :(
## 6: 7 0.11 :(
## 7: 7 0.27 :(
## 8: 6 0.052 .
## [1] 6.5
## [1] 0.756## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0490 32 0.069 .
## 2: 0.09375 0.1200 48 0.0073 **
## 3: 0.15625 0.0940 51 0.015 *
## 4: 0.21875 0.0310 47 0.34 :(
## 5: 0.28125 0.0020 46 0.97 :(
## 6: 0.34375 -0.0440 41 0.67 :(
## 7: 0.40625 -0.0063 44 0.77 :(
## 8: 0.46875 -0.0190 42 0.63 :(
## 9: 0.53125 -0.1600 34 0.0078 **
## 10: 0.59375 -0.2400 37 0.00021 ***
## 11: 0.65625 -0.1600 40 0.00065 ***
## 12: 0.71875 -0.2200 46 1.8e-06 ***
## 13: 0.78125 -0.2700 42 3.2e-06 ***
## 14: 0.84375 -0.2400 46 3.4e-07 ***
## 15: 0.90625 -0.2200 53 4.6e-08 ***
## 16: 0.96875 -0.1300 55 3.7e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 32 0.069 .
## 2: 48 0.0073 **
## 3: 51 0.015 *
## 4: 47 0.34 :(
## 5: 46 0.97 :(
## 6: 41 0.67 :(
## 7: 44 0.77 :(
## 8: 42 0.63 :(
## 9: 34 0.0078 **
## 10: 37 0.00021 ***
## 11: 40 0.00065 ***
## 12: 46 1.8e-06 ***
## 13: 42 3.2e-06 ***
## 14: 46 3.4e-07 ***
## 15: 53 4.6e-08 ***
## 16: 55 3.7e-07 ***
## [1] 44
## [1] 6.4
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0810 10 0.041 *
## 2: 0.09375 0.0062 10 1 :(
## 3: 0.15625 0.0940 9 0.81 :(
## 4: 0.21875 -0.0190 5 1 :(
## 5: 0.28125 0.0022 8 0.83 :(
## 6: 0.34375 0.0063 6 0.83 :(
## 7: 0.40625 -0.1100 7 0.55 :(
## 8: 0.46875 -0.1200 9 0.23 :(
## 9: 0.53125 -0.0430 7 0.45 :(
## 10: 0.59375 -0.2400 6 0.21 :(
## 11: 0.65625 -0.2100 5 0.28 :(
## 12: 0.71875 -0.3400 9 0.012 *
## 13: 0.78125 -0.2800 7 0.051 .
## 14: 0.84375 -0.2400 7 0.074 .
## 15: 0.90625 -0.1600 9 0.075 .
## 16: 0.96875 -0.0850 10 0.18 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 10 0.041 *
## 2: 10 1 :(
## 3: 9 0.81 :(
## 4: 5 1 :(
## 5: 8 0.83 :(
## 6: 6 0.83 :(
## 7: 7 0.55 :(
## 8: 9 0.23 :(
## 9: 7 0.45 :(
## 10: 6 0.21 :(
## 11: 5 0.28 :(
## 12: 9 0.012 *
## 13: 7 0.051 .
## 14: 7 0.074 .
## 15: 9 0.075 .
## 16: 10 0.18 :(
## [1] 7.75
## [1] 1.73
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0190 19 0.88 :(
## 2: 0.09375 0.0900 27 0.028 *
## 3: 0.15625 0.0021 26 1 :(
## 4: 0.21875 -0.0520 24 0.46 :(
## 5: 0.28125 -0.0310 22 0.34 :(
## 6: 0.34375 -0.0570 19 0.45 :(
## 7: 0.40625 -0.0560 22 0.36 :(
## 8: 0.46875 -0.0190 23 0.7 :(
## 9: 0.53125 -0.2600 18 0.013 *
## 10: 0.59375 -0.2400 20 0.0073 **
## 11: 0.65625 -0.1600 22 0.026 *
## 12: 0.71875 -0.2200 21 0.0016 **
## 13: 0.78125 -0.1900 19 0.01 *
## 14: 0.84375 -0.2700 24 0.00018 ***
## 15: 0.90625 -0.2300 27 5e-05 ***
## 16: 0.96875 -0.0960 27 0.0023 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 19 0.88 :(
## 2: 27 0.028 *
## 3: 26 1 :(
## 4: 24 0.46 :(
## 5: 22 0.34 :(
## 6: 19 0.45 :(
## 7: 22 0.36 :(
## 8: 23 0.7 :(
## 9: 18 0.013 *
## 10: 20 0.0073 **
## 11: 22 0.026 *
## 12: 21 0.0016 **
## 13: 19 0.01 *
## 14: 24 0.00018 ***
## 15: 27 5e-05 ***
## 16: 27 0.0023 **
## [1] 22.5
## [1] 3.1
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 3 NA
## 2: 0.09375 0.160 11 0.035 *
## 3: 0.15625 0.270 16 0.0023 **
## 4: 0.21875 0.110 18 0.032 *
## 5: 0.28125 0.035 16 0.36 :(
## 6: 0.34375 -0.028 16 1 :(
## 7: 0.40625 0.094 15 0.067 .
## 8: 0.46875 0.031 10 0.3 :(
## 9: 0.53125 -0.031 9 0.4 :(
## 10: 0.59375 -0.170 11 0.026 *
## 11: 0.65625 -0.160 13 0.017 *
## 12: 0.71875 -0.220 16 0.01 *
## 13: 0.78125 -0.280 16 0.0013 **
## 14: 0.84375 -0.240 15 0.0029 **
## 15: 0.90625 -0.240 17 0.0018 **
## 16: 0.96875 -0.220 18 0.00032 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.035 *
## 2: 16 0.0023 **
## 3: 18 0.032 *
## 4: 16 0.36 :(
## 5: 16 1 :(
## 6: 15 0.067 .
## 7: 10 0.3 :(
## 8: 9 0.4 :(
## 9: 11 0.026 *
## 10: 13 0.017 *
## 11: 16 0.01 *
## 12: 16 0.0013 **
## 13: 15 0.0029 **
## 14: 17 0.0018 **
## 15: 18 0.00032 ***
## [1] 14.5
## [1] 2.92## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.1400 38 0.0033 **
## 2: 0.09375 0.1800 43 8.1e-06 ***
## 3: 0.15625 0.2000 48 3.1e-06 ***
## 4: 0.21875 0.1900 50 2e-06 ***
## 5: 0.28125 0.2200 49 6.1e-05 ***
## 6: 0.34375 0.1800 41 2.3e-05 ***
## 7: 0.40625 0.0940 47 0.011 *
## 8: 0.46875 0.0310 47 0.0053 **
## 9: 0.53125 -0.0310 45 0.087 .
## 10: 0.59375 0.0062 46 0.86 :(
## 11: 0.65625 -0.0810 42 0.15 :(
## 12: 0.71875 -0.1700 41 0.0011 **
## 13: 0.78125 -0.1600 53 7.2e-06 ***
## 14: 0.84375 -0.2400 52 1.4e-08 ***
## 15: 0.90625 -0.2600 42 1.2e-07 ***
## 16: 0.96875 -0.3900 29 2.6e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 38 0.0033 **
## 2: 43 8.1e-06 ***
## 3: 48 3.1e-06 ***
## 4: 50 2e-06 ***
## 5: 49 6.1e-05 ***
## 6: 41 2.3e-05 ***
## 7: 47 0.011 *
## 8: 47 0.0053 **
## 9: 45 0.087 .
## 10: 46 0.86 :(
## 11: 42 0.15 :(
## 12: 41 0.0011 **
## 13: 53 7.2e-06 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.2e-07 ***
## 16: 29 2.6e-06 ***
## [1] 44.6
## [1] 5.93
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.094 29 0.032 *
## 2: 0.09375 0.140 29 4e-04 ***
## 3: 0.15625 0.140 28 0.0017 **
## 4: 0.21875 0.160 26 0.0036 **
## 5: 0.28125 0.180 24 0.039 *
## 6: 0.34375 0.160 21 0.022 *
## 7: 0.40625 0.069 21 0.14 :(
## 8: 0.46875 0.031 25 0.076 .
## 9: 0.53125 -0.031 23 0.49 :(
## 10: 0.59375 -0.094 23 0.12 :(
## 11: 0.65625 -0.056 22 0.45 :(
## 12: 0.71875 -0.160 20 0.037 *
## 13: 0.78125 -0.160 27 0.0018 **
## 14: 0.84375 -0.250 25 9e-05 ***
## 15: 0.90625 -0.310 15 0.0024 **
## 16: 0.96875 -0.240 2 0.5 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 29 0.032 *
## 2: 29 4e-04 ***
## 3: 28 0.0017 **
## 4: 26 0.0036 **
## 5: 24 0.039 *
## 6: 21 0.022 *
## 7: 21 0.14 :(
## 8: 25 0.076 .
## 9: 23 0.49 :(
## 10: 23 0.12 :(
## 11: 22 0.45 :(
## 12: 20 0.037 *
## 13: 27 0.0018 **
## 14: 25 9e-05 ***
## 15: 15 0.0024 **
## 16: 2 0.5 :(
## [1] 22.5
## [1] 6.58
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.220 9 0.055 .
## 2: 0.09375 0.310 13 0.0038 **
## 3: 0.15625 0.340 16 0.0011 **
## 4: 0.21875 0.210 18 0.005 **
## 5: 0.28125 0.220 17 0.0055 **
## 6: 0.34375 0.160 14 0.0029 **
## 7: 0.40625 0.094 16 0.044 *
## 8: 0.46875 0.031 14 0.36 :(
## 9: 0.53125 -0.031 14 0.3 :(
## 10: 0.59375 0.081 14 0.11 :(
## 11: 0.65625 -0.160 13 0.024 *
## 12: 0.71875 -0.170 15 0.078 .
## 13: 0.78125 -0.210 17 0.0014 **
## 14: 0.84375 -0.260 17 0.00049 ***
## 15: 0.90625 -0.270 17 0.00041 ***
## 16: 0.96875 -0.420 17 3e-04 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.055 .
## 2: 13 0.0038 **
## 3: 16 0.0011 **
## 4: 18 0.005 **
## 5: 17 0.0055 **
## 6: 14 0.0029 **
## 7: 16 0.044 *
## 8: 14 0.36 :(
## 9: 14 0.3 :(
## 10: 14 0.11 :(
## 11: 13 0.024 *
## 12: 15 0.078 .
## 13: 17 0.0014 **
## 14: 17 0.00049 ***
## 15: 17 0.00041 ***
## 16: 17 3e-04 ***
## [1] 15.1
## [1] 2.29
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 1 NA
## 3: 0.15625 0.290 4 0.58 :(
## 4: 0.21875 0.280 6 0.034 *
## 5: 0.28125 0.290 8 0.041 *
## 6: 0.34375 0.410 6 0.036 *
## 7: 0.40625 0.094 10 0.41 :(
## 8: 0.46875 0.130 8 0.0078 **
## 9: 0.53125 -0.031 8 0.098 .
## 10: 0.59375 0.056 9 0.34 :(
## 11: 0.65625 0.077 7 0.55 :(
## 12: 0.71875 -0.170 6 0.058 .
## 13: 0.78125 -0.056 9 0.4 :(
## 14: 0.84375 -0.240 10 0.032 *
## 15: 0.90625 -0.240 10 0.019 *
## 16: 0.96875 -0.380 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 4 0.58 :(
## 2: 6 0.034 *
## 3: 8 0.041 *
## 4: 6 0.036 *
## 5: 10 0.41 :(
## 6: 8 0.0078 **
## 7: 8 0.098 .
## 8: 9 0.34 :(
## 9: 7 0.55 :(
## 10: 6 0.058 .
## 11: 9 0.4 :(
## 12: 10 0.032 *
## 13: 10 0.019 *
## 14: 10 0.0059 **
## [1] 7.93
## [1] 1.9## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71942 -0.17247 0.00783 0.17452 0.64036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.168653 0.015973 10.56 <2e-16 ***
## timeNorm 0.007875 0.017892 0.44 0.66
## obj.diff -0.353796 0.024499 -14.44 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05568373)
##
## Null deviance: 116.20 on 1880 degrees of freedom
## Residual deviance: 104.57 on 1878 degrees of freedom
## AIC: -89.41
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78054 -0.20402 -0.04881 0.24891 0.83542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09780 0.01419 6.890 7.67e-12 ***
## timeNorm 0.04961 0.02071 2.395 0.0167 *
## obj.diff -0.35946 0.01812 -19.840 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07049446)
##
## Null deviance: 155.50 on 1814 degrees of freedom
## Residual deviance: 127.74 on 1812 degrees of freedom
## AIC: 341.96
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71114 -0.19255 -0.00268 0.18201 0.72417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24232 0.01477 16.412 < 2e-16 ***
## timeNorm 0.05282 0.01983 2.664 0.00779 **
## obj.diff -0.56482 0.01910 -29.567 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06780909)
##
## Null deviance: 188.61 on 1880 degrees of freedom
## Residual deviance: 127.35 on 1878 degrees of freedom
## AIC: 281.16
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4438596 0.4457102 0.0017643322 342 0.89 :(
## 2: 4.5 0.5374269 0.5624859 -0.0193532613 171 0.35 :(
## 3: 7.5 0.5157895 0.5357049 -0.0159697343 171 0.41 :(
## 4: 10.5 0.5421053 0.5362058 0.0148489779 171 0.46 :(
## 5: 13.5 0.5157895 0.5133937 0.0051408312 171 0.8 :(
## 6: 16.5 0.5315789 0.5320036 0.0012773881 171 0.95 :(
## 7: 19.5 0.5064327 0.5358363 -0.0298109502 171 0.1 :(
## 8: 22.5 0.4871345 0.4961373 -0.0092826000 171 0.66 :(
## 9: 25.5 0.4888889 0.4868060 -0.0006626257 171 0.98 :(
## 10: 28.5 0.4736842 0.4657574 0.0047061353 171 0.81 :(
## time error.diff shapes
## 1: 1.5 0.0017643322 16
## 2: 4.5 -0.0193532613 16
## 3: 7.5 -0.0159697343 16
## 4: 10.5 0.0148489779 16
## 5: 13.5 0.0051408312 16
## 6: 16.5 0.0012773881 16
## 7: 19.5 -0.0298109502 16
## 8: 22.5 -0.0092826000 16
## 9: 25.5 -0.0006626257 16
## 10: 28.5 0.0047061353 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3309091 0.3121174 0.01322078 330 0.53 :(
## 2: 4.5 0.5115152 0.6623901 -0.14555654 165 5.5e-10 ***
## 3: 7.5 0.4606061 0.5756691 -0.11526777 165 1.3e-06 ***
## 4: 10.5 0.5145455 0.6169890 -0.10480311 165 7.5e-06 ***
## 5: 13.5 0.4721212 0.5882784 -0.10892892 165 1.7e-07 ***
## 6: 16.5 0.4327273 0.5480044 -0.12509592 165 6e-07 ***
## 7: 19.5 0.4824242 0.5706900 -0.08307269 165 2.6e-05 ***
## 8: 22.5 0.5018182 0.5568448 -0.05086146 165 0.019 *
## 9: 25.5 0.5424242 0.5635905 -0.01007155 165 0.61 :(
## 10: 28.5 0.5084848 0.5525507 -0.04380158 165 0.048 *
## time error.diff shapes
## 1: 1.5 0.01322078 16
## 2: 4.5 -0.14555654 24
## 3: 7.5 -0.11526777 24
## 4: 10.5 -0.10480311 24
## 5: 13.5 -0.10892892 24
## 6: 16.5 -0.12509592 24
## 7: 19.5 -0.08307269 24
## 8: 22.5 -0.05086146 24
## 9: 25.5 -0.01007155 16
## 10: 28.5 -0.04380158 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3938596 0.3515160 0.050094933 342 0.016 *
## 2: 4.5 0.5081871 0.6513076 -0.158414133 171 7.7e-09 ***
## 3: 7.5 0.5093567 0.5682979 -0.068502095 171 0.0063 **
## 4: 10.5 0.5204678 0.5388474 -0.021601840 171 0.38 :(
## 5: 13.5 0.5157895 0.5225795 -0.010574437 171 0.66 :(
## 6: 16.5 0.5093567 0.5042410 -0.004080511 171 0.87 :(
## 7: 19.5 0.4614035 0.4415088 0.014455090 171 0.54 :(
## 8: 22.5 0.4280702 0.4078173 0.014864320 171 0.56 :(
## 9: 25.5 0.4614035 0.3856125 0.082930837 171 0.0017 **
## 10: 28.5 0.4485380 0.3513216 0.090016969 171 0.00063 ***
## time error.diff shapes
## 1: 1.5 0.050094933 24
## 2: 4.5 -0.158414133 24
## 3: 7.5 -0.068502095 24
## 4: 10.5 -0.021601840 16
## 5: 13.5 -0.010574437 16
## 6: 16.5 -0.004080511 16
## 7: 19.5 0.014455090 16
## 8: 22.5 0.014864320 16
## 9: 25.5 0.082930837 24
## 10: 28.5 0.090016969 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.75694 -0.18360 -0.03019 0.21428 0.58010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.26136 0.02137 12.227 < 2e-16 ***
## timeNorm 0.08303 0.02434 3.412 0.000668 ***
## obj.diff -0.58091 0.02545 -22.829 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06314505)
##
## Null deviance: 106.026 on 1154 degrees of freedom
## Residual deviance: 72.743 on 1152 degrees of freedom
## AIC: 92.263
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72990 -0.21256 -0.00011 0.21367 0.76833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17318 0.01398 12.387 <2e-16 ***
## timeNorm 0.04440 0.01810 2.452 0.0143 *
## obj.diff -0.43955 0.01914 -22.960 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06970934)
##
## Null deviance: 197.63 on 2309 degrees of freedom
## Residual deviance: 160.82 on 2307 degrees of freedom
## AIC: 407.99
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68710 -0.20239 -0.00475 0.20535 0.79944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15361 0.01286 11.942 <2e-16 ***
## timeNorm 0.03029 0.01787 1.695 0.0903 .
## obj.diff -0.37043 0.02033 -18.218 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06229074)
##
## Null deviance: 152.40 on 2111 degrees of freedom
## Residual deviance: 131.37 on 2109 degrees of freedom
## AIC: 135.8
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4909524 0.5078590 -0.02461793 210 0.31 :(
## 2: 4.5 0.5714286 0.7854497 -0.22469049 105 2.1e-10 ***
## 3: 7.5 0.5980952 0.7571408 -0.17682529 105 1.6e-07 ***
## 4: 10.5 0.6428571 0.7378391 -0.10178489 105 0.0023 **
## 5: 13.5 0.5895238 0.7194038 -0.15488488 105 8.6e-06 ***
## 6: 16.5 0.5780952 0.6928809 -0.12865340 105 0.00019 ***
## 7: 19.5 0.5895238 0.6519999 -0.06758259 105 0.012 *
## 8: 22.5 0.5980952 0.6672242 -0.06606931 105 0.025 *
## 9: 25.5 0.6019048 0.6374488 -0.03868620 105 0.3 :(
## 10: 28.5 0.6161905 0.6321308 -0.01384501 105 0.64 :(
## time error.diff shapes
## 1: 1.5 -0.02461793 16
## 2: 4.5 -0.22469049 24
## 3: 7.5 -0.17682529 24
## 4: 10.5 -0.10178489 24
## 5: 13.5 -0.15488488 24
## 6: 16.5 -0.12865340 24
## 7: 19.5 -0.06758259 24
## 8: 22.5 -0.06606931 24
## 9: 25.5 -0.03868620 16
## 10: 28.5 -0.01384501 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3892857 0.3833571 0.009187871 420 0.55 :(
## 2: 4.5 0.5438095 0.6644303 -0.118334829 210 5.7e-09 ***
## 3: 7.5 0.5009524 0.5546648 -0.057738067 210 0.0055 **
## 4: 10.5 0.5323810 0.5856428 -0.055862994 210 0.012 *
## 5: 13.5 0.5238095 0.5711545 -0.049345354 210 0.013 *
## 6: 16.5 0.5080952 0.5645710 -0.059023543 210 0.0031 **
## 7: 19.5 0.5085714 0.5763408 -0.070409217 210 0.00038 ***
## 8: 22.5 0.4742857 0.5129160 -0.047311259 210 0.034 *
## 9: 25.5 0.5309524 0.5222933 0.008175683 210 0.7 :(
## 10: 28.5 0.4976190 0.5011792 -0.015917435 210 0.43 :(
## time error.diff shapes
## 1: 1.5 0.009187871 16
## 2: 4.5 -0.118334829 24
## 3: 7.5 -0.057738067 24
## 4: 10.5 -0.055862994 24
## 5: 13.5 -0.049345354 24
## 6: 16.5 -0.059023543 24
## 7: 19.5 -0.070409217 24
## 8: 22.5 -0.047311259 24
## 9: 25.5 0.008175683 16
## 10: 28.5 -0.015917435 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3361979 0.2812232 0.05805815 384 0.00043 ***
## 2: 4.5 0.4635417 0.4940130 -0.02971391 192 0.17 :(
## 3: 7.5 0.4338542 0.4572421 -0.01981303 192 0.34 :(
## 4: 10.5 0.4546875 0.4436417 0.01569938 192 0.39 :(
## 5: 13.5 0.4291667 0.4100912 0.02898757 192 0.17 :(
## 6: 16.5 0.4270833 0.3974278 0.02804448 192 0.2 :(
## 7: 19.5 0.3979167 0.3739496 0.02162764 192 0.31 :(
## 8: 22.5 0.4005208 0.3577330 0.04141614 192 0.033 *
## 9: 25.5 0.4026042 0.3414701 0.05789652 192 0.0017 **
## 10: 28.5 0.3770833 0.3086979 0.06005258 192 0.002 **
## time error.diff shapes
## 1: 1.5 0.05805815 24
## 2: 4.5 -0.02971391 16
## 3: 7.5 -0.01981303 16
## 4: 10.5 0.01569938 16
## 5: 13.5 0.02898757 16
## 6: 16.5 0.02804448 16
## 7: 19.5 0.02162764 16
## 8: 22.5 0.04141614 24
## 9: 25.5 0.05789652 24
## 10: 28.5 0.06005258 24
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68318 -0.17197 -0.06763 0.20953 0.44308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.26214 0.07667 3.419 0.000745 ***
## timeNorm 0.03922 0.04490 0.873 0.383385
## obj.diff -0.52604 0.09657 -5.448 1.32e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04276234)
##
## Null deviance: 11.0260 on 230 degrees of freedom
## Residual deviance: 9.7498 on 228 degrees of freedom
## AIC: -67.605
##
## Number of Fisher Scoring iterations: 2## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5928571 0.7170177 -0.13220955 42 0.0011 **
## 2: 4.5 0.6571429 0.7946114 -0.14995732 21 0.019 *
## 3: 7.5 0.6380952 0.7649789 -0.12991685 21 0.035 *
## 4: 10.5 0.6571429 0.7869246 -0.13826606 21 0.026 *
## 5: 13.5 0.6428571 0.8120284 -0.18251913 21 0.0016 **
## 6: 16.5 0.6571429 0.7887369 -0.14411936 21 0.06 .
## 7: 19.5 0.6761905 0.7250289 -0.04778299 21 0.29 :(
## 8: 22.5 0.6666667 0.7637626 -0.10072169 21 0.1 :(
## 9: 25.5 0.6809524 0.8157609 -0.13535368 21 0.013 *
## 10: 28.5 0.6333333 0.7674702 -0.11749764 21 0.042 *
## time error.diff shapes
## 1: 1.5 -0.13220955 24
## 2: 4.5 -0.14995732 24
## 3: 7.5 -0.12991685 24
## 4: 10.5 -0.13826606 24
## 5: 13.5 -0.18251913 24
## 6: 16.5 -0.14411936 16
## 7: 19.5 -0.04778299 16
## 8: 22.5 -0.10072169 16
## 9: 25.5 -0.13535368 24
## 10: 28.5 -0.11749764 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70317 -0.16896 0.01375 0.17698 0.64802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18709 0.02966 6.307 4.63e-10 ***
## timeNorm -0.01154 0.02965 -0.389 0.697
## obj.diff -0.38631 0.04612 -8.375 2.36e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0670456)
##
## Null deviance: 59.836 on 824 degrees of freedom
## Residual deviance: 55.111 on 822 degrees of freedom
## AIC: 116.78
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4600000 0.4703926 -0.004017585 150 0.91 :(
## 2: 4.5 0.5626667 0.6181213 -0.049929670 75 0.12 :(
## 3: 7.5 0.5386667 0.5405554 0.007318205 75 0.84 :(
## 4: 10.5 0.5426667 0.5682867 -0.012295430 75 0.75 :(
## 5: 13.5 0.5506667 0.5516441 -0.004940452 75 0.87 :(
## 6: 16.5 0.5440000 0.5685882 -0.030883251 75 0.31 :(
## 7: 19.5 0.4946667 0.5794923 -0.085106719 75 0.003 **
## 8: 22.5 0.4706667 0.5231952 -0.058919282 75 0.072 .
## 9: 25.5 0.5013333 0.5079792 -0.014719847 75 0.71 :(
## 10: 28.5 0.5013333 0.5148979 -0.027308081 75 0.35 :(
## time error.diff shapes
## 1: 1.5 -0.004017585 16
## 2: 4.5 -0.049929670 16
## 3: 7.5 0.007318205 16
## 4: 10.5 -0.012295430 16
## 5: 13.5 -0.004940452 16
## 6: 16.5 -0.030883251 16
## 7: 19.5 -0.085106719 24
## 8: 22.5 -0.058919282 16
## 9: 25.5 -0.014719847 16
## 10: 28.5 -0.027308081 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61865 -0.16483 0.01371 0.17764 0.56340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11206 0.02131 5.260 1.84e-07 ***
## timeNorm 0.02484 0.02475 1.004 0.316
## obj.diff -0.19452 0.03920 -4.963 8.45e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04664526)
##
## Null deviance: 39.558 on 824 degrees of freedom
## Residual deviance: 38.342 on 822 degrees of freedom
## AIC: -182.53
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3860000 0.3450617 0.042653637 150 0.021 *
## 2: 4.5 0.4786667 0.4418554 0.043104906 75 0.14 :(
## 3: 7.5 0.4586667 0.4666577 -0.005389581 75 0.86 :(
## 4: 10.5 0.5093333 0.4339237 0.083907158 75 0.0024 **
## 5: 13.5 0.4453333 0.3915256 0.062218949 75 0.021 *
## 6: 16.5 0.4840000 0.4235337 0.063525179 75 0.015 *
## 7: 19.5 0.4706667 0.4392064 0.031920568 75 0.25 :(
## 8: 22.5 0.4533333 0.3941442 0.062224272 75 0.026 *
## 9: 25.5 0.4226667 0.3735255 0.050458512 75 0.05 .
## 10: 28.5 0.4013333 0.3321373 0.066488010 75 0.0078 **
## time error.diff shapes
## 1: 1.5 0.042653637 24
## 2: 4.5 0.043104906 16
## 3: 7.5 -0.005389581 16
## 4: 10.5 0.083907158 24
## 5: 13.5 0.062218949 24
## 6: 16.5 0.063525179 24
## 7: 19.5 0.031920568 16
## 8: 22.5 0.062224272 24
## 9: 25.5 0.050458512 16
## 10: 28.5 0.066488010 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74353 -0.20874 0.00921 0.20654 0.60833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22508 0.02572 8.752 <2e-16 ***
## timeNorm 0.03968 0.03440 1.154 0.249
## obj.diff -0.51555 0.03132 -16.460 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06419126)
##
## Null deviance: 55.359 on 593 degrees of freedom
## Residual deviance: 37.937 on 591 degrees of freedom
## AIC: 59.634
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4361111 0.3647578 0.07303014 108 0.039 *
## 2: 4.5 0.5592593 0.7356615 -0.18434054 54 0.00014 ***
## 3: 7.5 0.5574074 0.7147027 -0.17041673 54 0.00024 ***
## 4: 10.5 0.6314815 0.7227008 -0.08581209 54 0.047 *
## 5: 13.5 0.5351852 0.6812848 -0.14784579 54 0.00032 ***
## 6: 16.5 0.4907407 0.6163897 -0.15815934 54 0.005 **
## 7: 19.5 0.5129630 0.6057373 -0.10594419 54 0.014 *
## 8: 22.5 0.6129630 0.6322030 -0.01267083 54 0.83 :(
## 9: 25.5 0.5537037 0.5850422 -0.03200306 54 0.64 :(
## 10: 28.5 0.5611111 0.5894469 -0.01732044 54 0.63 :(
## time error.diff shapes
## 1: 1.5 0.07303014 24
## 2: 4.5 -0.18434054 24
## 3: 7.5 -0.17041673 24
## 4: 10.5 -0.08581209 24
## 5: 13.5 -0.14784579 24
## 6: 16.5 -0.15815934 24
## 7: 19.5 -0.10594419 24
## 8: 22.5 -0.01267083 16
## 9: 25.5 -0.03200306 16
## 10: 28.5 -0.01732044 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74906 -0.19267 -0.05492 0.23540 0.86516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06264 0.02036 3.076 0.00216 **
## timeNorm 0.05239 0.03029 1.729 0.08410 .
## obj.diff -0.30669 0.02664 -11.513 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07338495)
##
## Null deviance: 74.897 on 890 degrees of freedom
## Residual deviance: 65.166 on 888 degrees of freedom
## AIC: 206.22
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3000000 0.3038003 -0.021976446 162 0.57 :(
## 2: 4.5 0.5086420 0.6435586 -0.118822900 81 1.9e-05 ***
## 3: 7.5 0.4296296 0.5229068 -0.099048300 81 0.0065 **
## 4: 10.5 0.4740741 0.6031879 -0.132293983 81 0.00011 ***
## 5: 13.5 0.4753086 0.5733030 -0.090316862 81 0.0029 **
## 6: 16.5 0.4333333 0.5345376 -0.094416022 81 0.0032 **
## 7: 19.5 0.5222222 0.5914733 -0.055143049 81 0.07 .
## 8: 22.5 0.4283951 0.5257793 -0.094106034 81 0.00089 ***
## 9: 25.5 0.5580247 0.5797558 -0.008270062 81 0.69 :(
## 10: 28.5 0.5160494 0.5619786 -0.059751925 81 0.048 *
## time error.diff shapes
## 1: 1.5 -0.021976446 16
## 2: 4.5 -0.118822900 24
## 3: 7.5 -0.099048300 24
## 4: 10.5 -0.132293983 24
## 5: 13.5 -0.090316862 24
## 6: 16.5 -0.094416022 24
## 7: 19.5 -0.055143049 16
## 8: 22.5 -0.094106034 24
## 9: 25.5 -0.008270062 16
## 10: 28.5 -0.059751925 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64976 -0.15267 -0.07143 0.24828 0.73575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03381 0.03009 1.124 0.262
## timeNorm 0.05708 0.04702 1.214 0.226
## obj.diff -0.30079 0.04072 -7.387 1.26e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06611709)
##
## Null deviance: 25.23 on 329 degrees of freedom
## Residual deviance: 21.62 on 327 degrees of freedom
## AIC: 45.097
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2250000 0.2398207 -0.02972150 60 0.32 :(
## 2: 4.5 0.4333333 0.5813467 -0.14387812 30 0.0099 **
## 3: 7.5 0.3700000 0.4678668 -0.07002244 30 0.1 :(
## 4: 10.5 0.4133333 0.4639707 -0.06867710 30 0.17 :(
## 5: 13.5 0.3500000 0.4613004 -0.07486892 30 0.036 *
## 6: 16.5 0.3266667 0.4612711 -0.14672095 30 0.0032 **
## 7: 19.5 0.3200000 0.4514902 -0.11134531 30 0.0015 **
## 8: 22.5 0.5000000 0.5050770 0.01525165 30 0.81 :(
## 9: 25.5 0.4800000 0.4813309 0.01240702 30 0.63 :(
## 10: 28.5 0.3933333 0.4606821 -0.04485632 30 0.26 :(
## time error.diff shapes
## 1: 1.5 -0.02972150 16
## 2: 4.5 -0.14387812 24
## 3: 7.5 -0.07002244 16
## 4: 10.5 -0.06867710 16
## 5: 13.5 -0.07486892 24
## 6: 16.5 -0.14672095 24
## 7: 19.5 -0.11134531 24
## 8: 22.5 0.01525165 16
## 9: 25.5 0.01240702 16
## 10: 28.5 -0.04485632 16
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71866 -0.13903 -0.07563 0.26274 0.50459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42377 0.05076 8.348 1.98e-15 ***
## timeNorm 0.14908 0.04803 3.104 0.00208 **
## obj.diff -0.83399 0.05725 -14.568 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06928841)
##
## Null deviance: 39.074 on 329 degrees of freedom
## Residual deviance: 22.657 on 327 degrees of freedom
## AIC: 60.558
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5183333 0.6190299 -0.11343824 60 0.035 *
## 2: 4.5 0.5333333 0.8686553 -0.40875076 30 4.7e-07 ***
## 3: 7.5 0.6433333 0.8280425 -0.21851448 30 0.0026 **
## 4: 10.5 0.6533333 0.7307282 -0.08113246 30 0.21 :(
## 5: 13.5 0.6500000 0.7231805 -0.11138569 30 0.31 :(
## 6: 16.5 0.6800000 0.7634659 -0.08833518 30 0.17 :(
## 7: 19.5 0.6666667 0.6841523 -0.01454153 30 0.89 :(
## 8: 22.5 0.5233333 0.6626856 -0.13957220 30 0.019 *
## 9: 25.5 0.6333333 0.6069621 0.04686400 30 0.67 :(
## 10: 28.5 0.7033333 0.6142242 0.09037720 30 0.18 :(
## time error.diff shapes
## 1: 1.5 -0.11343824 24
## 2: 4.5 -0.40875076 24
## 3: 7.5 -0.21851448 24
## 4: 10.5 -0.08113246 16
## 5: 13.5 -0.11138569 16
## 6: 16.5 -0.08833518 16
## 7: 19.5 -0.01454153 16
## 8: 22.5 -0.13957220 24
## 9: 25.5 0.04686400 16
## 10: 28.5 0.09037720 16## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61859 -0.10903 -0.00671 0.07862 0.58880
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36691 0.02494 14.710 <2e-16 ***
## timeNorm 0.04858 0.03107 1.563 0.118
## obj.diff -0.74625 0.03169 -23.552 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0527652)
##
## Null deviance: 61.012 on 593 degrees of freedom
## Residual deviance: 31.184 on 591 degrees of freedom
## AIC: -56.799
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4250000 0.3818097 0.04228909 108 0.095 .
## 2: 4.5 0.5703704 0.7600559 -0.20253863 54 3.2e-05 ***
## 3: 7.5 0.5555556 0.6218983 -0.09003742 54 0.046 *
## 4: 10.5 0.6055556 0.5834307 0.00810360 54 0.84 :(
## 5: 13.5 0.5592593 0.5950296 -0.04254806 54 0.29 :(
## 6: 16.5 0.5703704 0.6040416 -0.04104681 54 0.27 :(
## 7: 19.5 0.5074074 0.5492649 -0.04685252 54 0.26 :(
## 8: 22.5 0.5481481 0.4793441 0.07132819 54 0.15 :(
## 9: 25.5 0.5314815 0.4559805 0.08431132 54 0.073 .
## 10: 28.5 0.4648148 0.3909264 0.07818103 54 0.17 :(
## time error.diff shapes
## 1: 1.5 0.04228909 16
## 2: 4.5 -0.20253863 24
## 3: 7.5 -0.09003742 24
## 4: 10.5 0.00810360 16
## 5: 13.5 -0.04254806 16
## 6: 16.5 -0.04104681 16
## 7: 19.5 -0.04685252 16
## 8: 22.5 0.07132819 16
## 9: 25.5 0.08431132 16
## 10: 28.5 0.07818103 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64341 -0.23071 -0.01188 0.22146 0.76386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19659 0.01984 9.911 <2e-16 ***
## timeNorm 0.01537 0.02837 0.542 0.588
## obj.diff -0.46502 0.03048 -15.257 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07026932)
##
## Null deviance: 83.775 on 956 degrees of freedom
## Residual deviance: 67.037 on 954 degrees of freedom
## AIC: 179.61
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3316092 0.2404667 0.10902062 174 0.0013 **
## 2: 4.5 0.4609195 0.5088613 -0.05709143 87 0.12 :(
## 3: 7.5 0.4344828 0.4454615 -0.01096261 87 0.77 :(
## 4: 10.5 0.4218391 0.4450093 -0.02077395 87 0.54 :(
## 5: 13.5 0.4425287 0.4084377 0.04437001 87 0.25 :(
## 6: 16.5 0.4126437 0.3529078 0.05198053 87 0.13 :(
## 7: 19.5 0.3620690 0.2909555 0.06629129 87 0.052 .
## 8: 22.5 0.3206897 0.2755358 0.02510627 87 0.36 :(
## 9: 25.5 0.3586207 0.2656083 0.09568731 87 0.0079 **
## 10: 28.5 0.3505747 0.2360832 0.10488729 87 0.0058 **
## time error.diff shapes
## 1: 1.5 0.10902062 24
## 2: 4.5 -0.05709143 16
## 3: 7.5 -0.01096261 16
## 4: 10.5 -0.02077395 16
## 5: 13.5 0.04437001 16
## 6: 16.5 0.05198053 16
## 7: 19.5 0.06629129 16
## 8: 22.5 0.02510627 16
## 9: 25.5 0.09568731 24
## 10: 28.5 0.10488729 24
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.82635 -0.18617 0.00915 0.18749 0.74346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01651 0.01130 1.462 0.1440
## est.confidence.norm -0.04691 0.02004 -2.341 0.0193 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06166035)
##
## Null deviance: 116.20 on 1880 degrees of freedom
## Residual deviance: 115.86 on 1879 degrees of freedom
## AIC: 101.36
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.91528 -0.19153 0.00036 0.15203 0.97111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06361 0.01379 -4.613 4.25e-06 ***
## est.confidence.norm -0.01256 0.02323 -0.541 0.589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08575694)
##
## Null deviance: 155.50 on 1814 degrees of freedom
## Residual deviance: 155.48 on 1813 degrees of freedom
## AIC: 696.67
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.97361 -0.21791 -0.03643 0.23892 0.92021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03505 0.01483 2.363 0.0182 *
## est.confidence.norm -0.05646 0.02589 -2.181 0.0293 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1001246)
##
## Null deviance: 188.61 on 1880 degrees of freedom
## Residual deviance: 188.13 on 1879 degrees of freedom
## AIC: 1013.2
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1420.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4944 -0.6644 -0.0047 0.6469 4.3628
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01073 0.1036
## Residual 0.07329 0.2707
## Number of obs: 5577, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.999e-03 1.583e-02 8.900e+01 0.253 0.801141
## est.confidence.norm -4.890e-02 1.455e-02 5.467e+03 -3.361 0.000782 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.457
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -691.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2105 -0.6953 -0.0188 0.7233 3.1576
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02599 0.1612
## Residual 0.03662 0.1914
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02102 0.02488 90.70000 -0.845 0.400
## est.confidence.norm 0.03031 0.02465 1796.40000 1.229 0.219
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.482
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 577.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0934 -0.6742 0.0414 0.6087 3.9364
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01013 0.1006
## Residual 0.07607 0.2758
## Number of obs: 1815, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08917 0.02229 146.70000 -4.001 9.97e-05 ***
## est.confidence.norm 0.03712 0.03197 634.90000 1.161 0.246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.738
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 832.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9937 -0.7008 -0.0459 0.6928 3.6148
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01624 0.1274
## Residual 0.08543 0.2923
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02690 0.02503 141.90000 -1.075 0.2842
## est.confidence.norm 0.06773 0.03449 1078.00000 1.964 0.0498 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.688## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1420.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4944 -0.6644 -0.0047 0.6469 4.3628
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01073 0.1036
## Residual 0.07329 0.2707
## Number of obs: 5577, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.999e-03 1.583e-02 8.900e+01 0.253 0.801141
## est.confidence.norm -4.890e-02 1.455e-02 5.467e+03 -3.361 0.000782 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.457
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -691.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2105 -0.6953 -0.0188 0.7233 3.1576
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02599 0.1612
## Residual 0.03662 0.1914
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02102 0.02488 90.70000 -0.845 0.400
## est.confidence.norm 0.03031 0.02465 1796.40000 1.229 0.219
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.482
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 577.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0934 -0.6742 0.0414 0.6087 3.9364
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01013 0.1006
## Residual 0.07607 0.2758
## Number of obs: 1815, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08917 0.02229 146.70000 -4.001 9.97e-05 ***
## est.confidence.norm 0.03712 0.03197 634.90000 1.161 0.246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.738
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 832.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9937 -0.7008 -0.0459 0.6928 3.6148
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01624 0.1274
## Residual 0.08543 0.2923
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02690 0.02503 141.90000 -1.075 0.2842
## est.confidence.norm 0.06773 0.03449 1078.00000 1.964 0.0498 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.688Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.14956, p-value = 0.8811
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.008949093Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.4555, p-value = 0.01407
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1448659
##
## [1] "pbg.on.error -0.14 0.014 *"In game level and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3326, p-value = 0.1827
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06943578
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.5094, p-value = 0.6105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04636591
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.48639, p-value = 0.6267
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04511785
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.4731, p-value = 0.1407
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1340852Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.8222, p-value = 0.00477
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1793215
##
## [1] "sexe.on.error 0.18 0.0048 **"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6928, p-value = 0.09049
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1863138
##
## [1] "sexe.on.error.m 0.19 0.09 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6144, p-value = 0.1064
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1809973
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5451, p-value = 0.1223
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1700543
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3700, p-value = 0.05818
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.00174298 0.07072221
## sample estimates:
## difference in location
## 0.03823549
##
## [1] "sexe.on.error.2 0.038 0.058 . mean(A): -0.038 mean(B): 0.001"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 431, p-value = 0.2418
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.02759275 0.10536605
## sample estimates:
## difference in location
## 0.04159407
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 393, p-value = 0.2888
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.02906997 0.09784528
## sample estimates:
## difference in location
## 0.03658899
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 409, p-value = 0.3268
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.03067049 0.09164194
## sample estimates:
## difference in location
## 0.03311583Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.20783, p-value = 0.8354
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01175452
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.049556, p-value = 0.9605
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.004872842
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.066909, p-value = 0.9467
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.006667408
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.32559, p-value = 0.7447
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03198445Self efficacy and subjective difficulty error
## Warning: Removed 84 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.9186, p-value = 0.003516
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2241957
##
## [1] "self.eff.on.error -0.22 0.0035 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6067, p-value = 0.1081
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2186683## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.686, p-value = 0.09179
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2294667
##
## [1] "self.eff.on.error -0.23 0.092 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6779, p-value = 0.09338
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2245285
##
## [1] "self.eff.on.error -0.22 0.093 ."